Shipping an LLM feature is not about finding the perfect prompt. It’s about turning an inherently probabilistic component into a system with clear boundaries, measurable quality, and predictable failure modes.
Below is a practical checklist you can use to move from prototype to product. Treat it like a design review template: if you can’t answer a section, you’ve found your next engineering task.
1) Start by making the feature legible
Before architecture, write down the job the model is doing.
Define the task in one sentence
- Example: “Given a customer email, draft a reply that follows our policy and cites the relevant order details.”
Define input/output contracts
- Inputs: what fields, which are optional, max lengths, language expectations.
- Outputs: format (plain text vs structured JSON), required sections, allowed tone.
Define what ‘good’ means
- Quality: correctness, completeness, adherence to policy.
- UX: latency budget, streaming vs non-streaming, user edit flows.
- Risk: what is unacceptable (PII leaks, unsafe instructions, fabricated policy).
If the team can’t articulate these, you will end up “debugging vibes” later.
2) Choose the minimum viable LLM architecture
Most teams over-complicate too early. Pick the simplest design that supports the contract.
Common patterns (in order of simplicity)
- Prompt-only: fixed prompt + user input. Works for low-stakes summarization or rewriting.
- RAG (retrieval-augmented generation): retrieve relevant docs, ground the response.
- Tool calling: model selects functions (search, database lookup, policy checker), your code executes.
- Multi-step agents: iterative planning/execution. Use only when tasks truly require branching.
Practical guidance
- Prefer RAG + tool calling over autonomous agents for production workflows.
- Make the “system” deterministic where possible: retrieval, tool execution, post-processing.
- Constrain outputs with structured schemas (JSON schema / function calling) when downstream systems rely on it.
3) Build guardrails where they actually work
Guardrails are not a single filter at the end. They are layered constraints across the pipeline.
Input guardrails
- PII detection/redaction if prompts can contain sensitive data.
- Injection-aware formatting: clearly separate user content from instructions.
Retrieval guardrails (for RAG)
- Only retrieve from approved sources.
- Store provenance (doc id, chunk id, timestamp) to support auditing.
Generation guardrails
- Use structured outputs when possible.
- Keep temperature low for tasks requiring precision.
- Enforce maximum output length; long outputs correlate with drift.
Post-generation validation
- Validate JSON schema.
- Run policy checks (regex + classifier where appropriate).
- For high-stakes: require citations or tool evidence.
Guardrails should be testable. If you can’t write a unit test for a guardrail, it’s likely wishful thinking.
4) Treat evaluation as a product requirement, not research
You need a feedback loop that survives beyond the initial launch.
Create an evaluation set early
Start with 50–200 representative examples.
- Pull from real user data when possible (with privacy controls).
- Cover edge cases: long inputs, ambiguous requests, conflicting sources.
- Include negative cases: “should refuse,” “should ask a clarifying question,” “should not answer.”
Define metrics that map to user value
Avoid a single “LLM score.” Use a small scorecard:
- Task success (did it solve the user’s problem?)
- Factuality / grounding (is it supported by sources/tools?)
- Policy compliance (no disallowed content)
- Format correctness (schema valid, required fields present)
- Latency (p50/p95 end-to-end)
Use a mix of eval methods
- Golden tests for regression detection.
- LLM-as-judge for scalable scoring (calibrate on human labels).
- Human review for periodic audits and ambiguous cases.
The goal is not academic benchmarking. The goal is to prevent silent quality regressions.
5) Engineer prompts like interfaces
A production prompt is closer to an API contract than creative writing.
Practical prompt hygiene
- Separate concerns: instructions, context, examples, user input.
- Use stable delimiters and explicit roles.
- Keep prompts versioned (and log which version served each request).
Few-shot examples: use sparingly
Examples can improve reliability but increase token cost and brittleness. Prefer programmatic constraints (schema + validators) when possible.
6) Design for failure: what happens when the model is wrong?
LLMs will be wrong. Your system should fail in predictable ways.
Add a “safe fallback” path
- Ask a clarifying question.
- Offer a template response.
- Route to human support.
Expose uncertainty thoughtfully
Instead of “I’m not sure,” use actionable UX:
- “I found two possible answers—choose the correct order.”
- “I can draft a response, but you must confirm the refund policy.”
Log the right artifacts
For each request, capture:
- Prompt version, model version
- Retrieved document ids
- Tool calls and outputs
- Validation outcomes
- Final response
This makes debugging concrete and supports audits.
7) Control cost and latency intentionally
Cost and latency aren’t afterthoughts; they affect UX and unit economics.
High-leverage tactics
- Cache retrieval results and/or final outputs for repeat queries.
- Use smaller models for classification, routing, extraction.
- Stream responses when user-perceived latency matters.
- Set token budgets per stage (retrieval context, reasoning, final output).
Model routing
Start with a simple policy:
- Use a cheaper model by default.
- Escalate to a stronger model when validators fail or inputs are complex.
8) Put it behind a release process
Treat model behavior changes like code changes.
- Version prompts, retrieval indices, and model configurations.
- Use staged rollouts (internal → small % → full).
- Add “kill switches” and feature flags.
- Create a lightweight incident playbook: what to do on hallucination spikes, tool failures, or policy violations.
9) Decide how you’ll maintain it
Once it ships, it becomes a living subsystem.
Maintenance questions to answer now
- How often will you refresh documents/embeddings?
- Who reviews flagged outputs, and how often?
- What triggers a prompt/model change? Who approves?
- How will you detect drift (quality, topic shift, new policy)?
A final rule of thumb
If your LLM feature can’t be described as a set of contracts, validators, and measurable metrics, it’s still a prototype.
Use this checklist in your next design review. You’ll ship fewer surprises—and when surprises happen, you’ll have the instrumentation to fix them quickly.