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AI for product research: from interview analysis to validation · 1 / 3

Use three illustrated review cards to understand observation, interpretation, themes and samples.

AI for product research: from interview analysis to validation · 2 / 3

Turn a hypothesis into an observable task and distinguish independent success from assisted completion.

AI for product research: from interview analysis to validation · 3 / 3

Classify thirteen interview, review and observation records with AI while deduplicating participants. Correct a recorded causal overstatement and recheck the memo against new version and access-policy evidence.

For developers: AI in your code and products · 1 / 6

Using the Claude API as the example: keep your API key safe, send a first request from Python, and read the answer, the stop reason and the token usage.

For developers: AI in your code and products · 2 / 6

Give context, work in small steps, verify with tests — a workflow that makes AI-written code something you can trust.

For developers: AI in your code and products · 3 / 6

Instead of asking for JSON and hoping, define the shape as a schema and get responses your code can use directly. Worked through with a customer-message classifier.

For developers: AI in your code and products · 4 / 6

Cache the long instructions and documents you resend with every request, and their input cost drops sharply. Using a support bot built on a manual, here's how to place the cache point and check that it works.

For developers: AI in your code and products · 5 / 6

Ask AI for a retrieval comparison, revise it using two real failures, then distinguish development questions from fresh checks in a complete runnable example.

For developers: AI in your code and products · 6 / 6

Learn to validate AI tool proposals, repair invalid JSON, then replay a lost reply after draft creation without duplicating the result.