About to ship an LLM feature? Use this 9-step checklist to reduce risk and ship faster—drawing on field-tested patterns and practitioner notes from Simon Willison and other industry guidance.
The 9-step LLM launch checklist
- Pin the job-to-be-done and a single success metric. Write one sentence about the user task and one KPI you’ll move (e.g., +10% self‑serve resolution rate). For risk framing, see the NIST AI Risk Management Framework.
- Threat model the flow. Map inputs, tools, data stores, and outputs. Decide what the model is allowed to do and where it can’t go. Use the OWASP Top 10 for LLM Applications to spot common failure modes.
- Protect data boundaries. Strip PII and secrets, enforce retrieval filters, and apply least-privilege to data sources. Deny-by-default for external URLs and tools unless explicitly allow‑listed.
- Harden against prompt injection and tool abuse. Treat all retrieved text as untrusted. Lock system prompts, restrict tool schemas, escape user content, and validate outputs. Read OpenAI’s prompt‑injection guidance.
- Build a minimal eval harness. Create golden sets and adversarial tests (toxic content, jailbreaks, canaries). Track accuracy, refusal correctness, latency, and cost. For context on evaluation breadth, see Stanford’s HELM.
- Add human‑in‑the‑loop for risky actions. Require confirmations for writes/transactions, stage changes before publish, and rate‑limit high‑impact tools.
- Wire observability and privacy‑aware telemetry. Log model, versioned prompt, input type, tool calls, tokens, cost, and p95/p99 latency. Hash/anonymize user data and rotate logs.
- Set budgets and fallbacks. Cap token usage, cache responses, prefer smaller models for routing, stream partial answers, and route to a deterministic fallback on failure or policy violation.
- Version and rollback. Version prompts, RAG indexes, and models. Ship behind a feature flag with one‑click rollback and a kill switch.
Takeaway
A lightweight checklist beats a fragile demo. Define the job, lock down inputs and tools, test with evals, watch costs/latency, and ship behind flags so you can learn—and safely undo.
Further reading: Simon Willison on pragmatic LLM engineering, OWASP LLM Top 10, OpenAI on prompt injection, and the NIST AI RMF.
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