Context Engineering for AI UX: A Practical Checklist

Hard truth: LLMs don’t “know” things. They complete text based on the context you give them.

So when your AI feature fails, the correct first question is not “which model?”
It’s: “What did the model see?”

Context engineering is everything the model sees before it answers: system rules, chat history, retrieved docs, tool results, UI metadata, schemas. Design the context well and the “intelligence” suddenly looks better.

The AI UX Failure Pattern

Most teams ship an AI feature like this:

1. Add a prompt
2. Add a text box
3. Pray

And then the failures arrive:

• Confident nonsense
• Missing critical steps
• Vague advice
• “It depends” spam
• Random formatting

This isn’t a prompt problem. It’s an application-layer problem.

The Checklist

1) Define the job (one sentence)

If you can’t define the job, you can’t evaluate output quality.

Template:

• “Given X, produce Y, optimized for Z, under constraints C.”

Example:

• “Given a URL, produce an actionable UX audit with prioritized issues, including accessibility, clarity, and conversion, in a fixed JSON schema.”

2) Provide the minimum necessary context (not the maximum)

More context often makes results worse due to attention dilution.

Rule of thumb:

• Put stable rules in the system prompt
• Retrieve volatile facts just-in-time
• Summarize everything else

3) Give the model tools (and tell it when to use them)

Tool use is where “agentic” behavior actually comes from.

type Tool = {
  name: string;
  description: string;
  inputSchema: Record<string, any>;
};

Then write a policy:

• “If you are missing factual info, call fetch_page_snapshot.”
• “If you need structured checks, call run_a11y_scan.”
• “Never guess metrics you can measure.”

4) Force a schema for outputs

If your UX depends on the model “being tidy”, you’re gambling.

Use a schema. Keep it boring.

{
  "summary": "string",
  "top_issues": [
    {
      "category": "accessibility|usability|clarity|performance|conversion",
      "severity": "critical|high|medium|low",
      "evidence": "string",
      "fix": "string"
    }
  ],
  "next_steps": ["string"]
}

5) Separate reasoning from results

You want users to see evidence, not chain-of-thought.

Design outputs as:

• Claim
• Evidence
• Fix
• Confidence (optional)

Example:

• Claim: Form labels are missing.
• Evidence: Inputs lack <label> or aria-label.
• Fix: Add labels and connect with htmlFor.

6) Add “trust UX” on critical actions

If the model can trigger changes, add friction.

• Confirmation
• Preview diffs
• “Explain what you’re about to do”
• “Show your sources”
• Safe defaults

7) Log context like you log errors

If you can’t reproduce the context, you can’t debug the AI.

Log:

• Prompt version hash
• Retrieved doc IDs
• Tool calls + outputs
• Output schema version

A Minimal “AI UX Spec” You Can Copy

### AI Feature: {name}

**Job:** {one sentence}
**Inputs:** {list}
**Tools:** {list + policies}
**Output Schema:** {json schema}
**Quality Gates:** {must-pass checks}
**Fallback:** {what happens on low confidence}
**Observability:** {what we log}

Quality Gates That Actually Work

Fail the response if:

• Missing required fields
• No evidence provided for critical claims
• Severity assigned without justification
• No actionable fix provided
• Conflicts with tool outputs

This is how you keep the model honest: the app enforces reality.

Conclusion

Model upgrades help. But reliability is mostly a context + constraints game.

If you want better AI UX, stop prompt-tweaking and start shipping:

• tools
• schemas
• evidence
• logs

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