# AI-Assisted Research Walkthrough — Learn by Doing ## Before We Begin **Diagnostic:** What can AI *not* do well in research? When you feed 50 user feedback snippets to an LLM for synthesis, what might it get wrong—or miss entirely? Why does that matter? **Checkpoint:** You can name at least two limitations (e.g., confabulation, bias toward common themes, missing nuance) and why human validation is non-negotiable. --- ## Step 1: Prepare Raw Feedback **Task:** Gather 10–15 real or simulated user feedback snippets (support tickets, survey responses, or review quotes). Paste them into a single document. Remove or anonymize any PII. **Question:** Before using AI—if you had to synthesize these manually, what would you look for? Themes? Sentiment? Feature requests? **Checkpoint:** The user has a clean, anonymized set of feedback ready for synthesis. --- ## Step 2: Write a Synthesis Prompt **Task:** Write a prompt for Claude that will synthesize your feedback into themes. Include: (1) your role/context, (2) the format you want (e.g., table: Theme | Quote | Count), (3) a request to flag assumptions. **Question:** How might the prompt change if you wanted to focus only on *pain points* vs *feature requests*? What wording would you add? **Checkpoint:** The user has a prompt that specifies context, output format, and validation instructions. --- ## Step 3: Run Synthesis and Inspect **Task:** Run your prompt with Claude (or another AI). Inspect the output. For each theme: Can you trace it back to at least one source quote? Label any theme that feels like an AI inference vs grounded in your data. **Question:** Did the AI surface anything you hadn't noticed? Did it miss or oversimplify anything? **Checkpoint:** The user can distinguish AI-inferred themes from data-backed ones and identify gaps. --- ## Step 4: Validate With a Quick Check **Task:** Pick the top 2 themes from the synthesis. For each, write: (1) one piece of evidence that supports it, (2) one thing that would disprove it, (3) one question you'd ask in an interview to validate it. **Question:** If you couldn't use AI, would you have arrived at the same themes? What does that tell you about AI's role? **Checkpoint:** The user has a validation plan for at least 2 themes. --- ## Step 5: Draft a Competitor Analysis Prompt **Task:** You're analyzing 3 competitors. Write a prompt that asks Claude to produce a feature comparison table. Specify: columns (Feature | Competitor A | B | C | Us), rows (which features to compare), and a request to note where info might be outdated. **Question:** What would you need to verify manually after getting the table? (Pricing? Feature availability? Positioning?) **Checkpoint:** The user has a structured competitor-analysis prompt and knows what to validate. --- ## Step 6: Combine AI Output With Human Judgment **Task:** Take your synthesized themes and draft 3 interview questions that would validate or challenge them. Use the themes to focus the questions—don't let AI replace the need to talk to users. **Question:** How would you use the interview answers? Would you feed them back into AI for another round of synthesis? **Checkpoint:** The user connects AI synthesis to a human validation loop. --- ## Step 7: Document Assumptions and Gaps **Task:** Create a short "Research Readiness" checklist for your synthesis: What's validated? What's inferred? What's missing? What's the next step to reduce uncertainty? **Question:** If a stakeholder asked "How confident are you in these findings?", how would you answer using this checklist? **Checkpoint:** The user can articulate confidence levels and next validation steps.