Optimal Blog

‍This is Part 2 of our series, The System Behind Better Content, co-authored by Content Design Hub. You can read Part 1 here.

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A content spec is a structured, testable document that defines how content should look, sound, and behave across a product, built from real user research so your team (and your AI tools) have a shared set of rules to work from. In Part 1, we showed you how you can create a content spec and what it might look like, but having the spec is only half the work. A document sitting in a folder doesn't change anything on its own, it has to be operationalized to earn its place in your workflow.

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What’s inside

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• 7 places to add your spec so the whole team actually uses it
• What AI gets wrong without a spec, and a full example prompt to fix it
• The full spec cycle from research to AI guardrails to closing the loop

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Embedding a content spec into existing workflows 

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A content spec only delivers value if the whole team uses it. The spec makes that possible. If it’s in the spec, anyone can check against it. That includes product managers writing acceptance criteria, engineers writing automated test cases, and QA testers checking final screens.

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7 places you could add your spec to

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A spec needs to be embedded in the places where decisions happen. This could be your team’s:

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• Design system: make the spec into a component on Figma so content rules live where design happens.
• Product management tool: include spec compliance in your definition of done so that no ticket closes without a content spec check.
• Code repository: store the spec as Markdown (.md) in the repository so developers can reference it without leaving their workflow.
• AI project files:  upload the spec to Claude project files or in a new chat of your preferred AI tool as a Markdown (.md) with your brand and tone guidelines and keywords list. Structure your instructions to read and strictly adhere to the guidelines so the AI tool applies the approved language every time they generate content (more on this in section 4). 

• Sprint kickoffs: reference the spec when writing user stories and defining acceptance criteria.
• Definitions of done: add "content checked against spec" as a required condition before any feature ships.
• QA checklists: include spec compliance as a structured check alongside usability tests and live site tests

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The key is to keep the spec visible to your teammates and easy to reference wherever your team works, including the AI tools your team uses.

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Add AI guardrails: writing for your users, not someone else's defaults 

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When AI tools generate content, they use their training data, but much of the publicly available digital content used to develop AI systems is disproportionately produced in Western and North American contexts. These perspectives differ from the lived experiences, cultural contexts, and perspectives of the users you are designing for. 

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What AI gets wrong without a spec

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Without a spec grounded in user research, AI writing tools can produce content that is grammatically correct and seemingly polished, but culturally misaligned.

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For an Australian student finance app, that might look like: 

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• ‘Your 401(k) contributions’. In Australia, it’s called superannuation or super.
• ‘Apply for social security benefits’. In Australia, this is Centrelink. The term, ‘social security’ means something entirely different.
• ‘File your federal income tax’. In Australia, you lodge a tax return with the ATO (Australian Taxation Office).

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These are not AI trivial errors. Nor are they hallucinations. AI got these terms correct, but for a different audience.

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When content is not localized, you may not only alienate your users, but also create anxiety and erode trust. In a regulated industry like financial services, it can also create compliance risk.

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What the content spec does for AI 

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A content spec is one of the most effective prompt engineering assets your team can have. Prompt engineering is the practice of writing precise and reusable instructions that guide AI tools to produce consistent and predictable outputs.

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When you feed your spec rules into an AI prompt, you give the model the guardrails so it doesn’t fill in the blanks and default to its training data.

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Example prompt using a content spec

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Role

You’re a content designer writing for a money management app aimed at Australian young people aged 18 to 24.

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Context

Users may be managing money independently for the first time. They are familiar with informal language but need to learn formal financial terminology as part of using the app. Australian financial, tax, and welfare systems have specific terms that differ from other English-speaking countries.

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Task

Write content for the app onboarding flow. Users will experience this flow when they sign up for the first time. The flow includes the welcome screen and account setup steps. It’s the first time a user will encounter key financial concepts like superannuation, HECS-HELP, and Centrelink payments. Each screen should introduce one concept at a time, explain it in plain English, and tell the user what action to take next.

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Output

Screen-by-screen onboarding content, including headings, body copy, and button labels. Keep all sentences under 20 words. Each screen should have one heading, no more than 3 sentences of body copy, and one call-to-action.

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Constraints

Use the content spec to guide your content decisions.

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Use the acceptance criteria and user story to guide your deliverables.

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Key rules include:

• Use 'superannuation' on first mention, then 'super'
• Use 'Centrelink payment,' not 'government benefit' or 'welfare'
• Use 'lodge a tax return,' not 'file taxes'
• Use 'transaction account,' not 'checking account'
• Address users as 'you', never 'the user' or 'our customers'
• Do not use North American financial terms (401k, social security, federal income tax)
• Legal disclosures must use terminology required by ASIC guidelines and cannot be rewritten for style.

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Validation

Before finalising any content, check it against the content spec. If a term does not appear in the approved list, flag it for review rather than substituting a synonym.

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Confirmation

Before you draft the content, confirm you can:

• Access and read the spec.
• The screens you will create.
• The user you are writing for.
• User story and acceptance criteria.

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With these prompt rules, your AI tool has a strong starting point and will give you a better first draft. 

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The spec also helps teams evaluate AI output consistently. Instead of asking, “Does this sound right?’, you may question, “Does this align with our rules?”

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Courses like AI Prompt Engineering for Content Creators can be a useful way to learn how to write effective AI prompts grounded in content design principles.

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View content as a system, not a document

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A content spec is a living system that connects user research to design decisions, team workflows, and AI tools. It's like a muscle; the more you use it, the stronger it gets. The more you add to it, the more accurate, reliable, and valuable it becomes.

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Here's the full spec cycle:

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• Research: discover how your users think, talk, and group information.
• Spec creation: turn those findings into explicit, testable rules.
• Spec storage: add your spec to systems and places where it can be used and updated.
• Team alignment: embed the spec in design reviews, sprint kickoffs, and definitions of done.
• AI guardrails: feed the spec into AI prompts to get consistent, culturally appropriate and accurate outputs.
• Close the loop: when research reveals a gap between user language and spec language, update the spec. The updated spec flows through everywhere content decisions are made (design guidelines, AI prompts, QA checklists, and developer documentation).

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The content spec keeps your product human. It's rooted in research and reflects the context of your users; how they think, speak, and make sense of the world.

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AI is now a standard part of content workflows. This means that the spec is no longer optional and is no longer just an engineering tool. Without a content spec, AI tools default to someone else's language, someone else's culture, and someone else's assumptions.

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Product teams that invest in a content spec now are building a system that scales, learns, and most importantly, keeps the user at the centre of every decision.

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Start with research. Build the spec. Keep your users in every decision.

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Meet with us to learn how teams are using Optimal to transform interviews into insights and create content that truly connects. 

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This is Part 1 of our series, The System Behind Better Content, co-authored by Content Design Hub.

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This piece is a collaboration between Optimal and Content Design Hub, exploring how a content specification, or “content spec”, grounded in user research can give your team and AI tools the guardrails they need to create appropriate, consistent, and user-centered content at scale. A content spec ensures your content is structured the way your users think, making it easier for them to find what they need, understand it, and take action.

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In this article:

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• The difference between a content spec and a style guide, plus persistent vs. local specs
• Why discovery research is the input layer for every spec
• How to translate findings into explicit, testable rules, examples, and formats

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What is a spec, and how does it relate to content?

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Words and language are core to product design. Like any design decision, content needs a specification that gives your team—and your AI tools—the rules for creating consistent, user-centered content. This is known as a content spec.

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Defining a content spec

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A content spec is a structured document that defines how content should look, sound, and behave across a product.

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There are 2 types of content specs:

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• Persistent (global) specs:
  
  • rules that apply across every product, service, and channel you have. These rules cover things like approved terminology, grammar and accessibility standards. They don't change from product to product; they live at the brand level or in your design system.

• Local (contextual) specs:
  
  • rules for a specific product, feature, or user flow. They may include the words you use in your app’s navigation, how error messages are worded, or specific eligibility requirements users need to know. They inherit the rules from the global spec, adding the detail that's unique to that product.

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Definition

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Content spec: a structured, testable document that defines content rules, guidelines, templates and behaviours. It allows design teams to create repeatable content patterns that are consistent and can scale across different products and channels.

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What happens without a spec

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At a small scale, vague content guidance is manageable. You can run tests to validate your decisions, and you can make changes on the fly. However, when you’re working on products within a larger system, without a spec, your content won’t scale (with humans or AI).

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How to identify that your content is not scalable:

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• Different team members make different calls.
• AI tools fill in the gaps with generic defaults.
• Research findings show high levels of user confusion, misunderstanding and frustration
• Content decisions have no rationale and are not defensible.

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The fix already exists (just not in content design)

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Engineers solved this problem decades ago. Engineers work from specifications so that anyone on the team can build the same thing the same way. Content design needs to make the same shift.

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A content spec is the missing piece in most content design systems. And, it all starts with user research. Let us walk you through how to build, use, and maintain it.

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“Content is not a finishing touch; it’s the foundation of all digital products.” – Content Design Hub.

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Start with research: know your users before you write a word

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The content spec starts with research. Before you create a standard or pattern, you need to understand who your users are and what they need. That's the difference user research makes. It shows you how users really talk about their problems and needs, in their own words, so you can build the right solution and write content that sounds like your users. 

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Here’s an example:

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An Australian bank is building a money management app for young people aged 18 to 24. The app will help users track things like part-time income, savings, debt and scholarships. It aims to improve financial literacy and planning.

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Financial terminology is shaped by a country's legal, regulatory and tax systems. We can’t always simplify or eliminate those terms like “Low Income Tax Offset” (tax reduction) and “HECS-HELP” (student loan scheme), but the content should make them understandable and help users navigate confidently

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Terms all need to appear in the right context and with the right explanation.

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Discovery research is the input layer

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Before any content is written, the team runs discovery research. This is the input layer for the content spec. It will give your team the evidence base that everything else is built on. 

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The research mix might include: 

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• Interviews: understand how young people currently manage money. Identify how they think and talk about budgeting and spending. Explore what challenges they face, and what tools or habits they already use to stay on top of their finances.
• Card sorting: understand how young people naturally group and label financial content and get valuable insights to optimize navigation, menus, content, and information architecture.
• Live site testing: observe real behaviour on competitor apps to understand how young people currently navigate financial products.
• Surveys: get a deeper understanding of your users' needs, preferences, and pain points 
• Tree Testing: see where users get stuck in your current website or app and which labels cause confusion.

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Discovery research surfaces answers to four foundational questions that will guide your approach to content spec design: 

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1. Who are your users? 
2. What is their context?
3. What language do they use naturally?
4. What words do they use to describe the topic? 

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While these findings are genuinely interesting, they are also the raw material of your content specs.

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Turn research into a working document

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Once you have your research findings, the next step is to turn them into explicit, testable rules. This phase is where a content spec becomes distinct from a style guide.

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What a content spec includes 

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A content spec is a working document that defines: 

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• Tone and voice rules: how the product speaks to its users.
• Approved and prohibited language: specific terms to use, and specific terms to avoid.
• Label conventions: how navigation items, buttons, form fields, and error messages are named.
• Best and worst practice examples: concrete, side-by-side comparisons that leave no room for interpretation.
• Explicit rules: content that can be consistently applied and tested.

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Aspirational versus explicit rules

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There is a differentiation between a content style guide and a content spec. Most content style guides are broad and aspirational, not specific. A style guide has good principles, but they are not necessarily rules AI can consistently apply or test against. 

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Here are two of the same rules, but expressed differently in a style guide and a content spec.

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Example of a content style guide rule

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Our tone of voice is warm and helpful. 

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Example of a content spec: persistent (global)

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Our tone of voice is warm and helpful. 

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We:

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• Use contractions (you're, we've, let's).
• Keep sentences under 20 words.
• Address the user as "you," not "the user".
• Lead with what the user can do, not what the system can't.
• Never use passive voice in error messages.

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Example:

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Do not write: ‘Your application has been received and is pending review.’

Write: ‘We've got your application. We'll let you know within 2 business days.’

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Exceptions:

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This tone does not apply to terms and conditions, privacy policy, or legal disclosures. These sections must follow the language required by Australian financial services regulations and cannot be rewritten for style. Content that introduces this information must be written in plain language (words 2 syllables or less, with definitions of complex words).

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Example of a content spec: local (app contextual)

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• Use ‘superannuation’ on first mention, then ‘super’.
• Never use ‘retirement savings fund’.
• Always explain HECS-HELP on first use within a product flow as: ‘the government's interest-free student loan scheme’.
• Use ‘Centrelink payment’, not ‘government benefit’ or ‘welfare’.
• Use ‘part-time income’, not ‘gig income’ or ‘casual earnings’.

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File formats that make specs usable for humans and AI

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A content spec is most useful when it lives in a format that both humans and AI can read. You have three options to either store or export as your content spec.

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1. Markdown (.md): text-formatting language that’s easy to write, read, and version-control in tools like GitHub.
2. YAML (YAML Ain't Markup Language): a format for organizing structured data. It’s commonly used in configuration files and can be read by non-developers.
3. JSON schema: a way to define and validate the structure of content. It’s useful for automated checks and feeding rules directly into AI systems. 

For product teams, writing in these formats may seem a bit alien. So that’s where you can draft your rules as a .txt file or in a Word Document and then export it as a Markdown file.

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The format you give AI is important because AI reads plain text more reliably. A Word Document or PDF file has layers of code that can interfere with how AI tools parse (extract) content.

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Turn research findings into a content spec

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Adding research findings to a content spec removes the barrier between user research and product decision-making.

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Instead of a product manager checking abstract rules, they can trace a rule back to the user insight that generated it. This approach gives everyone in a team more context into how and why decisions are made. And of course, all these decisions are tied back to the user.

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You can easily create a content spec using MCP. Connect your research repository to your preferred AI tool (such as Claude, ChatGPT, or Cursor) and use MCP to pull relevant insights and evidence directly from your research. From there, turn that research into a structured content spec that can live in different workspaces and tools for easy reference. 

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What your card sorting findings look like

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Participants aged 18 to 25 were given 30 cards covering financial concepts. A product team asked the participants to group the concepts and name each group in their own words.

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Findings:

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• 19 of 24 participants grouped ‘superannuation,’ ‘employer contributions,’ and ‘retirement savings’
• 8 participants renamed the category ‘superannuation’ to ‘super’.
• 10 participants renamed ‘employer contributions’ to ‘pay’, while 7 renamed it to ‘salary’.
• The most common category labels were ‘savings for the future’ (9 participants), ‘long-term savings’ (6 participants), and ‘locked funds’ (4 participants).
• No participant under 21 used the word ‘retirement’ unprompted.

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Insights:

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• Young people are familiar with ‘super’ as a shorthand, but don't connect it to retirement.
• ‘Employer contributions’ reads as income. Participants view this category in terms of payroll language ("pay," "salary") rather than savings language. 
• Institutional framing of financial terms does not match how this age group thinks about money.
• When re-labelling content, plain language was used. Pronouns and verbs were not present.

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What your Navigation label spec entry looks like

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Next in Series

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A content spec built from real research gives your team a shared source of truth, but a document sitting in a folder doesn't change anything on its own. The value comes from operationalizing it. 

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In Part 2, we cover how to operationalize a content spec in 2026 so it actually gets used. You'll get example prompt rules to help you craft guardrails for your AI tool to help you produce consistent draft content.

Optimal gets used for a lot of research that nobody outside the team ever sees. A card sort that reshapes a navigation menu. A week of usability sessions that kills a feature before it ships. We started the Optimal Experience Awards to put some of that work in front of an audience for once, and this year's entries made the judges' job hard in the best way.

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This year the panel scored hundreds of entries across six categories, with 14 judges reading every submission against the same rubric. Judges weighed the method behind the work as much as what came of it.

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Why we do this

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Good research tends to disappear into the decision it supported. That's sort of the point of it. A good outcome looks obvious once it ships, and nobody outside the room remembers the months of interviews and testing that got the team there. It makes the work hard to point to later. A new researcher can't easily learn from something they never got to see.

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The Optimal Experience Awards is one way to work against that. We wanted the researcher who spent a year winning back a skeptical stakeholder to get the same spotlight as whatever they eventually helped ship. Reading the studies in the open, including the parts that didn't work, makes everyone doing this work sharper.

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How judging worked

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Every entry got scored by more than one judge, working independently, against six criteria specific to its category. Research for Social Good weighed participant protection and replicability heavily. In Outstanding User Research Project, rigor carried more weight, along with how well the method fit the problem. Those scores got combined into a per-category ranking.

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Now, the results.

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Outstanding User Research Project

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This category comes down to one project, how it was built and whether anyone downstream could use what it found.

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Winner:  EE

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Finalists: Southern New Hampshire University, Google, Contentsquare

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Research-Driven Impact

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Here the panel looked past the study design and asked what changed because the research happened.

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Winner:  Paper Leaf

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Finalists: Joruney Digital, Drexel University

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UX Research Leader of the Year

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This category looks at one person's body of work, the practice they built and the people they brought up behind them.

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Winner:  Dawn Ta, Director of Research & Design at Scientella

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Finalists: Dr. Meg Kurdziolek, Sarah Geden, Aline Lin

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Collaborative Research Excellence

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This category looks for collaboration that shaped how the research got built, across teams or organizations, from the ground up.

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Winner: Oxford University

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Finalists: Thailand Development Research Institute (TDRI), London Borough of Camden, Miro

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Research for Social Good

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Judges paid close attention to how well participants were protected here, and to how much the findings ended up helping the people the study was about.

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Winner: Base22 LLC

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Finalists: Analog Devices, Minnesota IT Services

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Best Use of Optimal

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This category rewards how well the tools fit the study, and what came out of that choice.

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Winner: NHS England

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Finalists: American Electric Power, Cengage, Eaton

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Thank You

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Thanks to everyone who put a project in front of a panel of people they don't know. That's not a small thing to do.

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Thanks to the judges too, for the time they spent going through every entry closely enough to argue about a few of them: Graham Gardner (U.S. Bank), Addison English, Harry Parkes (Hargreaves Lansdowne), Erietta Sapounakis (Stan.), Tradd Salvo (Huge), John Rainey (Gallos Technologies), David Vuu (Telstra Health), Joann Wu (Uber), Alexander Wilson (T. Rowe Price), Kate Towsey (The ResearchOps Review), Christina Goldschmidt (Warner Music Group).

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If your team did work this year that belongs on this list next time, we want to see it.

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Self-serve recruitment made participant recruitment faster and more affordable, but it didn’t completely eliminate the challenge of finding targeted, engaged, and honest participants. In our recent webinar, we called out what 100 research teams said about how they recruit, and used what we learned to build the next evolution of Optimal Recruitment.

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Summary

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• 75% of research teams say finding the right participants affects study quality or timelines.
• Self-serve tools made recruitment faster and cheaper, but shifted the work of screening, verification, chasing, and replacing participants
• 41% of researchers encounter fraudulent participants, while others struggle with participants who match criteria but provide poor-quality responses.
• Niche audiences are often difficult because multiple common criteria become rare when combined.
• The fix is a recruitment approach that combines multiple sources with automated checks and human in the loop to find reliable, high-quality participants.

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The 4 biggest challenges of participant recruitment

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1. You can't find the right people

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75% of teams say that not finding the right people impacts either their study quality or their timelines, and two in five have research that simply doesn't happen because they can't source the right participants.

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2. Some participants aren't who they say they are

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41% of researchers run into fraudulent participants, often using VPNs to present as multiple people, and knowledgeable enough to talk vaguely around a topic and slide past quality checks without saying anything specific or true.

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3. The right participant on paper isn't always the right participant in practice

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You can nail the segment, role, and behavior criteria, and still end up with someone who can't articulate a clear thought. That's pushed teams toward workarounds like video screeners to gauge whether someone is introspective and genuinely who they claim to be. Automation is good at matching criteria, but it can’t judge whether someone is introspective and would provide quality data.

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4. Stacked criteria make audiences hard to find

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Many audiences we call niche aren't actually made up of individually rare criteria. It's the combination of requirements that can make someone difficult to find. A specific role, at a certain company size, working in a specific function, using a specific tool are not criteria especially rare on their own. But layered together, the pool can get very small. 

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Solving these challenges takes a platform built to actually find the right people and verify them once they're found. 

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What is self-serve recruitment?

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Self-serve recruitment was created to make participant recruitment faster and easier. Instead of working with an agency, researchers can order participants directly based on the demographics and criteria they need for their study. But while this makes recruitment more accessible, it doesn’t eliminate the work behind the scenes. Teams still have to screen, chase, verify, and replace participants, and four out of five researchers still say the core problem is open.

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Part of the problem is the way self-serve recruitment is typically built. Most tools rely on a single panel and try to make every recruitment request fit within it. Instead, Optimal starts with who you actually need to recruit, then determines where those participants are most likely to be found. We call this a multi-panel model. Some panels are stronger for moderated B2B recruitment, others for general population studies, and others for regional coverage. By combining them, Optimal Recruitment gives you access to 20M+ verified participants across 150+ countries.

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And the data suggests that this broader access matters more than speed alone. In a study of 100 people, only 9% chose faster turnaround as the most important thing to improve. When asked to pick just one thing to fix, they prioritized access to hard-to-reach people, reliability, and better targeting.

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That’s why our focus isn’t just on getting participants into a study quickly. It’s on getting the right participants, from the right sources, with quality built into the recruitment process from start to finish.

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Ensuring participant quality

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Quality checks used to mean if they answered the screener correctly, but increasingly, it's about whether someone understood the task, engaged with it properly, and gave you something useful. Optimal Recruitment has layers of checks so you can find quality participants in every study:

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• Screeners to filter candidates before they begin your user research study
• Automated checks to flag straight-lining, repeated answers, and suspicious behavior patterns at scale
• Human in the loop catches what automation still can't, which matters more as research shifts toward formats like recorded usability tests

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Some teams want to submit an order and get straight into research. Others want more support: screener help, sourcing across panels, manual review, or a fully managed recruit. Neither is the "better" way to recruit. They're solving different problems.

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What we're doing is bringing those two worlds closer together.

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How to make a strong participant screener

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A better screener doesn't necessarily mean a longer screener It's more about being clever with the questions you're asking.

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Add a plausible but wrong option

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Rather than putting an answer or a red herring in that's really obviously fake and easy to spot, we like to include something that sounds like it could be right, but someone with genuine experience should know it isn't.

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Ask the same thing twice but in slightly different ways

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You don't obviously want to literally repeat the question, but you might validate something they told you earlier in the screener from a different angle later. If their experience is genuine, those answers should match up and make sense together. And if they don't, it's a really useful signal to take a closer look at the participant.

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Move more of your instructions into the screener itself, not just in the study

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Clear instructions are a huge contributor to participant quality, but sometimes what looks like a quality issue, it did actually start much earlier where the participant just didn't understand what they were signing up for. For example, in a recorded prototype test, state “this study requires you to speak your thoughts aloud as you complete the tasks, are you comfortable with that and in a quiet space?”; then, people who aren't comfortable can opt out before they ever get into the study.

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FAQ about Optimal Recruitment

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What are participant profiles?

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You can build recruitment profiles which group targeting criteria, like demographics, location, device, job title, seniority, company size, industry, and B2B, as well as screener questions to recruit for your studies. You can also  exclude participants you’ve already spoken to for new perspectives. 

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How many participants is enough?

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It depends on what you’re trying to learn. For usability testing, they tend to surface quickly, so around five participants per user group will often reveal almost all the problems. Concept testing may span a wider context, so you need a larger sample. 

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How do you ensure you are not hearing from the same people?

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We recently have added exclusion controls to participant profiles that lets you automatically exclude participants who've taken part in your studies within a set period; for example, the last three months. This ensures you aren't hearing from the same pool of people.

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How do you work to avoid participant fraud?

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Optimal ensures a 100% quality guarantee for participant responses by utilizing a multi-layered fraud prevention strategy that combines advanced technology with human oversight. Automated systems monitor participant behavior in real-time, detecting "speeders" by monitoring completion times, identifying skipping patterns or "straight-liners," and flagging any inconsistent responses. 

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Our award-winning panel partners maintain a fraud rate of less than 0.06% through advanced machine learning scoring systems (like PureScore) and AI-driven fraud detection, continuously screening participants. For any B2B studies, we will also cross check against LinkedIn profiles.

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Can Optimal help me recruit participants for my next study?

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Yes, Optimal can help you recruit the right participants for your study. You'll see a time estimate for when participants should start coming through after submitting a request.

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Recruitment doesn’t become a bottleneck. Your research can finally start moving at the speed of the questions you're actually trying to answer. 

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👉 If you want to experience the full walkthrough and demo of Optimal Recruitment, watch the full training webinar here.

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When we spoke to study creators about what can cause a study to fail, the answer was rarely a methodology problem. It was recruitment.

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If you've ever watched a study stall because the right participants didn't show up, you already know the problem we're trying to solve. The study is ready. The tasks are written. Stakeholders are waiting. And then recruitment breaks down. 

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Participants trickle in too slowly. The participant quality isn’t quite right. The timeline shifts. Confidence in the findings edges downward.

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Recruitment: a moving target

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Recruitment has always been a moving target. There are endless ways to improve it – through reach, participant quality, fraud prevention, and ease of use. 

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As part of our ongoing work to strengthen our recruitment offering, we’ve reimagined self-service recruitment at Optimal. We’ve taken a closer look at the experience and focused on giving research teams more options, greater control, and confidence in participant quality when recruiting participants.

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What we heard from research teams

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Across the teams we spoke to, several consistent themes emerged. Quality was a persistent concern. The issues were usually about fit. Participants sometimes didn’t meaningfully engage with the tasks or dropped off mid-session. 

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"Niche" participants were referenced often as well. What was usually meant wasn't rare profiles but it was a combination of specific targeting and filters applied at once. Seniority plus industry plus behaviour plus location. Multiple criteria that some existing tools just didn't handle well.

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On the coordination side, we heard a consistent ask: researchers want to know who they've already spoken to. They don't want to contact the same people repeatedly without realizing it. They're tired of managing participant lists manually just to avoid that problem.

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In short: speed matters, but predictability, visibility, and participant quality matter more.

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Built for real-world studies

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We've rebuilt Optimal’s self-service recruitment from the ground up.

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Participant Profiles: more flexibility and control

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The centerpiece of the new experience is Participant Profiles. Build a profile for exactly who you need from demographics, location (down to the city), job title, seniority, company size, device, industry, B2B, language, and more. You’ll also have the ability to:

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• Run multiple profiles in a single study. If you need senior decision-makers from the UK and individual contributors from the US, you can define both profiles with separate quotas and recruit them simultaneously for the same study.
  
  
• Set multiple quotas per profile. Define not just who you need, but how many of each. So if your study needs 30 brand managers and 20 operations managers, you can recruit for both.
  
  
• Set exclusion controls. One of the clearest things we heard: researchers don't want to keep recruiting the same people. It can create a bias in their data, but managing participant history manually can be resource-intensive and tedious. Exclusion controls let you automatically exclude participants who've taken part in your studies within a set period; for example, the last three months.

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More panels, more reach

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To help research teams reach the right participants with greater reliability, Optimal’s self-service recruitment now draws from multiple panels. This increases the pool of available participants across regions and segments, giving research teams more opportunities to find the right fit even for more specific or multi-criteria profiles.

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Access millions of participants across over 150 countries, with expanded targeting options beyond general population samples, including B2B audiences.

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Visibility and predictability, from setup to launch

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Before you launch, you'll see an estimated fill time, credit cost, and a feasibility check based on your profile and quotas. After launch, live recruitment progress sits alongside your study responses. You can see exactly how each profile quota is filling in real time.
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Why this matters now

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The teams we spoke to are running more ambitious studies than ever – recorded sessions, concept tests, and studies that sit between a quick survey and a full moderated interview. These studies demand reliable, targeted, well-matched participants. Recruitment is often the deciding factor in whether a study succeeds.

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Recruitment has always been the part of research that feels the least in your control. We want to change that, not adding more complexity, but by giving you the right tools to recruit exactly who you need.

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You should be able to see what's happening, trust that the right people are coming through, and stay focused on your study. Get the confidence in your participants so you can focus on what comes next: the decisions that matter.

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Profiles are now available in Optimal’s Usability Testing tool and will be rolling out across the rest of the platform.

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Explore the new recruitment experience in your account or book a demo.

Great design decisions are grounded in evidence. But finding the right evidence isn't always easy when it's spread across multiple teams, usability tests, interviews, and surveys.

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Instead of searching through reports or asking teammates if research already exists, Model Context Protocol (MCP) lets you ask questions about your research repository in natural language from AI tools like ChatGPT, Claude, Gemini, and Cursor.

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Whether you're designing a new feature, iterating on a prototype, or preparing for a design review, MCP helps you quickly bring user evidence into your workflow.

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Here are seven ways UX, product, and experience designers can use MCP throughout the design process.

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1. Start every design project with what users already told you

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Before opening Figma, understand what users are trying to accomplish, where they're struggling, and what your team has already learned.

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Try asking:

• What have we already learned about onboarding?
• What usability issues have we identified in checkout?
• What are users trying to achieve when managing their account settings?
• What research should I review before redesigning navigation?

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Designer workflow

Before kicking off a redesign, ask MCP to summarize existing research. Use the findings to define design goals, identify constraints, and prioritize the problems worth solving before creating your first wireframe.

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2. Validate design concepts before investing time in high-fidelity designs

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As ideas begin to take shape, use previous research to pressure-test your thinking. MCP can surface similar studies, recurring usability issues, and participant feedback that helps you refine concepts earlier.

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Try asking:

• Have we tested a similar design before?
• What patterns have users struggled with in previous prototypes?
• What should we avoid repeating?
• Which usability findings should influence this design?

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Designer workflow

While exploring concepts in Figma, keep an AI assistant open alongside your design files. Ask questions as you work so previous research continuously informs design decisions instead of becoming something you review once at the beginning.

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3. Write stronger design rationale

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Design reviews often involve explaining why a particular solution was chosen. Use MCP to find supporting evidence from previous studies.

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Try asking:

• Find participant quotes supporting a simplified navigation.
• What evidence suggests users prefer this workflow?
• Which usability studies identified this problem?
• Show examples of participants struggling with this interaction.

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Designer workflow

Use participant quotes, findings, and usability observations directly in design specs, PRDs, or design review presentations to help stakeholders understand the reasoning behind your decisions.

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4. Spot UX patterns across products and releases

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Looking across multiple studies can reveal broader experience patterns. MCP can identify recurring pain points, emerging behaviours, and themes that may influence future design priorities.

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Try asking:

• What usability issues appear across multiple product areas?
• Compare findings from our last five prototype tests.
• Which friction points have become more common over time?
• What navigation issues keep appearing across studies?

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Designer workflow

Before planning a larger redesign, review patterns across multiple past studies. These recurring themes often highlight systemic UX issues that individual projects miss.

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5. Prepare for design critiques and stakeholder reviews

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Strong design presentations combine visual solutions with user evidence. Use MCP to generate summaries tailored to your audience.

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Try asking:

• Summarize the research supporting this redesign.
• What are the three biggest user pain points?
• Create an executive summary for stakeholders.
• What customer evidence supports prioritizing this work?

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Designer workflow

Generate concise summaries before design critiques, roadmap discussions, or leadership reviews, then pair them with your prototypes to show both the solution and the evidence behind it.

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6. Plan better usability tests

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Designers frequently need to validate prototypes, but not every question requires a brand new study. MCP helps identify what has already been answered and where genuine knowledge gaps remain.

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Try asking:

• What questions about this flow are still unanswered?
• What assumptions should we validate?
• Which participant groups haven't been represented?
• What tasks should we include in our next prototype test?

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Designer workflow

Review previous findings before writing test tasks. Build studies that extend existing knowledge instead of repeating research your team has already completed.

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7. Bring research into the tools you already use

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Research is most valuable when it appears alongside the work you're already doing. With MCP, your repository becomes accessible from AI tools that support everyday design work.

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Potential workflows

• Generate a design brief from previous research before starting a new feature.
• Draft usability findings directly into Confluence or Notion.
• Format ideas into sticky notes and prep for a design sprint with Miro or Mural.
• Create presentation-ready summaries for design reviews.
• Turn research findings into product requirements for engineering.
• Compare proposed designs against historical usability findings.
• Ask follow-up research questions while designing in Figma with an AI assistant open alongside your work.

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Instead of switching between repositories, documents, and reports, research becomes part of your design process.

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Designing with confidence

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The best design decisions aren't based on intuition alone; they're informed by a deep understanding of user behavior.

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MCP makes it easier to bring research into everyday design work, helping you move from evidence to action faster. Whether you're exploring concepts, validating ideas, preparing stakeholder reviews, or planning usability tests, you can use MCP to help your research repository become an active design partner. 

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Book a demo or log into your account to get set up with MCP.