Shipping LLM features is an engineering problem first. The model is only one part of the system. Production quality depends on reliability, latency, cost, safety, and observability.
Start With a Clear Interface
- Inputs: Validate and normalize early (limits, allowed formats, policy checks)
- Outputs: Prefer structured outputs (JSON schema) and verify them before returning
- Failure modes: Define fallbacks (cached answers, simpler model, human escalation)
Latency and Cost Control
- Prompt discipline: Keep prompts short and predictable; avoid unbounded context
- Caching: Cache stable prompt fragments and common responses where safe
- Routing: Send simple queries to cheaper/faster models; reserve larger models for high-value paths
Reduce Hallucinations
- Grounding: Use retrieval when answers should come from your documents
- Guardrails: Enforce citation/justification policies when applicable
- Validation: Reject outputs that violate required structure or constraints
Observability You Actually Use
- Token usage and cost per request
- Latency percentiles (p50/p95/p99)
- Error categories (timeouts, policy blocks, parsing failures)
- Quality signals (user feedback, task success rate)
Practical tip: Add tracing and basic evals before you add features. Otherwise, you won't know what improved—or what regressed.