LLM features win when they’re reliable, fast, and predictable—not just clever. Use this compact checklist to de-risk your next AI release and keep users’ trust.
Prompted by a recent note on Simon Willison’s blog linking to engineer David Crawshaw (source), here’s a practical, tool-agnostic list you can apply today.
Before you code
- Define one job: Write the user story in one sentence and ban “and.”
- Pick a baseline: What’s the non-LLM alternative you must beat?
- Set a success metric: e.g., task success rate, time saved, or CSAT.
Build for reliability
- Structure I/O: Enforce schemas (JSON, XML) and validate before use.
- Scaffold prompts: Few-shot with real examples; keep prompts versioned.
- Time-bound calls: Timeouts, retries with jitter, and deterministic fallbacks.
- Cache aggressively: Deduplicate identical requests; store top results.
- Constrain tools: Limit functions the model can call; log every invocation.
Control cost and latency
- Set budgets: Max tokens per request and per user/session.
- Use the smallest model that passes your eval; escalate only on failure.
- Batch, stream, and precompute where possible to hide perceived latency.
Evaluate continuously
- Create a golden set: 20–200 real tasks with expected outputs.
- Automate regressions: Run evals on every prompt/model change.
- Close the loop: Capture user feedback, thumbs, and corrections as data.
Risk and safety defaults
- Guardrails: Filters for PII, abuse, and jailbreak patterns; safe fallbacks.
- Provenance: Display when content is AI-generated; link sources where possible.
- Privacy: Don’t log sensitive inputs; encrypt at rest; set strict retention.
When to upgrade the model
- Holdout tests: Compare new models on your golden set before rollout.
- Progressive delivery: Shadow, then 5% A/B, then ramp if metrics improve.
- Fail predictable: Prefer fail-closed for risky flows; fail-open for low risk UX.
Further reading: Simon Willison’s note linking to David Crawshaw (post) and the NIST AI Risk Management Framework (NIST AI RMF) for governance context.
Takeaway: Make it small, measurable, and reversible. Scope tightly, add guardrails, and ship with evals so improvements are obvious—and regressions are caught fast.
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