- Published:
- Last updated:
Building Reliable LLM Applications
- Authors
- Name
- Lucian Oprea
- @LucianDSA_
00:30:00
Filter by difficulty
36 of 36 questions shown
Application Contracts and Prompt Design
⏷ 1. What turns an LLM API call into a reliable application feature?
⏷ 2. How should system, developer, user, and retrieved instructions be separated?
⏷ 3. What makes a prompt a testable application contract?
⏷ 4. When should you use zero-shot, one-shot, and few-shot prompting?
⏷ 5. Why should prompts separate instructions, data, and output requirements?
Free preview complete
You’ve reached the end of the free preview
Get every remaining question and complete answer, plus progress tracking across the full Interview Question Library.
- 31 more questions and complete answers in this topic
- Full access to every interview topic
- Progress tracking and question flags
- New questions and improvements during your subscription
Full access from
$12/month
No long-term commitment. Cancel whenever you want.
⏷ 6. How do you manage prompt templates and versions?
⏷ 7. How do you budget tokens across instructions, context, history, tools, and output?
⏷ 8. What should you do when untrusted content contains instructions?
⏷ 9. When should application logic replace prompt instructions?
⏷ 10. How do you keep prompts concise without losing important constraints?
Structured Outputs and Tool Calls
⏷ 11. Why prefer structured outputs over parsing prose?
⏷ 12. What is the difference between JSON mode and schema-constrained output?
⏷ 13. How should you validate and repair malformed model output?
⏷ 14. What is tool/function calling, and what remains the application's responsibility?
⏷ 15. How should tool arguments be validated before execution?
⏷ 16. How do you design tool schemas that models can use reliably?
⏷ 17. How should an application handle multiple tool calls and tool-call loops?
⏷ 18. How do refusals, blocked responses, and incomplete outputs fit into the response contract?
Streaming and Conversation State
⏷ 19. What does streaming improve, and what does it not improve?
⏷ 20. How should a UI handle partial streamed output safely?
⏷ 21. How should conversation state be stored and reconstructed?
⏷ 22. When should you truncate, summarize, or retrieve older conversation turns?
⏷ 23. How do you prevent conversation summaries from silently corrupting state?
⏷ 24. How should cancellation and client disconnects propagate through the system?
Failure Handling and Provider Boundaries
⏷ 25. Which LLM failures are safe to retry?
⏷ 26. How do timeouts, exponential backoff, jitter, and retry budgets work together?
⏷ 27. How do idempotency keys protect tool calls and downstream side effects?
⏷ 28. How should you design a provider abstraction without hiding important capabilities?
Testing, Caching, and Production Design
⏷ 29. When is semantic/prompt caching safe, and what belongs in the cache key?
⏷ 30. How do you test a nondeterministic LLM feature and release changes safely?
Capacity, Safety, and Graceful Degradation
⏷ 31. How should rate limits, concurrency limits, and backpressure work in an LLM application?
⏷ 32. How should a fallback model or provider be designed without hiding a quality regression?
⏷ 33. How do you protect sensitive data across prompts, providers, logs, and evaluations?
⏷ 34. How should input and output safety controls be layered?
⏷ 35. When should you use model routing or a model cascade?
⏷ 36. How do you enforce cost budgets per request, workflow, and tenant?