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Assessment

Is Your LLM Integration Actually Production-Ready?

Reliability, cost, and UX - find out if your AI feature will hold up when things go sideways.

Includes a personalized analysis written by Seth based on 20+ years of engineering and startup experience.

20 questions · ~4 minutes

Question 1 of 20 Reliability & Fallbacks

What happens when the LLM API goes down or times out?

Question 2 of 20 Cost & Efficiency

Do you track token costs per user or per request?

Question 3 of 20 Reliability & Fallbacks

How do you version and deploy prompt changes?

Question 4 of 20 Observability & Quality

Do you have a way to evaluate whether the AI output is actually good?

Question 5 of 20 Observability & Quality

When did you last review your actual prompt in production - the exact string being sent to the API?

Question 6 of 20 Reliability & Fallbacks

Are you pinned to a specific model version?

Question 7 of 20 Reliability & Fallbacks

Do you have a plan for when your current model version gets deprecated?

Question 8 of 20 Reliability & Fallbacks

What happens when the LLM returns something unexpected or malformed?

Question 9 of 20 Reliability & Fallbacks

How do you handle conversations or documents that exceed the context window?

Question 10 of 20 User Experience

Do users understand what the AI is doing and why?

Question 11 of 20 User Experience

For long-running LLM calls, do you stream responses or block until complete?

Question 12 of 20 Cost & Efficiency

Do you rate limit LLM usage per user?

Question 13 of 20 Observability & Quality

Have you measured whether AI output is better than a simpler rule-based approach?

Question 14 of 20 Reliability & Fallbacks

Have you tested whether users can manipulate the AI's behavior through their inputs?

Question 15 of 20 User Experience

How do you handle latency? LLM calls can take several seconds.

Question 16 of 20 Cost & Efficiency

Do you cache LLM responses for repeated or similar queries?

Question 17 of 20 Observability & Quality

Do you log prompts and completions in production?

Question 18 of 20 Reliability & Fallbacks

For high-stakes AI outputs, is there a human review step before anything irreversible happens?

Question 19 of 20 User Experience

Can users correct or override the AI output?

Question 20 of 20 Cost & Efficiency

How much do you actually know about what your LLM integration costs per active user per month?

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