LLM Integration Checklist for Production

Demos are easy. Production is hard. After shipping dozens of LLM-powered features for clients in SaaS, healthcare, and fintech, we’ve compiled the checklist below. If you skip items on it, expect pain.

1. Define the job, not the model

Start with a one-paragraph spec: what the user is trying to do, what “good” looks like, and what the cost of failure is. Everything downstream (model choice, eval set, guardrails) flows from this. If you can’t write it, you’re not ready.

2. Build an evaluation set before you build the feature

Collect 50–200 representative inputs with expected outputs or rubrics.
Mix easy, hard, and adversarial cases.
Version the eval set alongside your code — it will change.
Automate offline evaluation in CI for every change.

If you only remember one thing: an LLM without an eval suite is a liability.

3. Pick the cheapest model that works

Run your eval against multiple tiers (e.g., GPT-4o-mini, Claude Haiku, Llama 3 8B) and plot quality vs. cost. The biggest model is almost never the right answer. Revisit this choice quarterly — prices and capabilities change.

4. Structured outputs are your friend

Use JSON schemas, function calling, or grammar-constrained decoding. Parsing free-form text is a tax you’ll pay every day. Add a validator and a single retry — don’t build a 5-layer fallback pyramid.

5. Retrieval: keep it boring

Start with a single dense index before adding hybrid search, rerankers, or graph retrieval.
Log every retrieved chunk. You’ll need it for debugging.
Test chunking strategies empirically — don’t copy a blog post.
If your corpus is small (< 10k docs), pgvector or even in-memory is fine.

6. Guardrails at every boundary

Input: PII detection and redaction; prompt-injection defences.
Output: policy checks, schema validation, toxicity filters where relevant.
Tools: allow-list for function calls, rate-limit external actions.

7. Observability: logs, traces, metrics

Instrument with OpenTelemetry or a vendor SDK (Langfuse, Helicone, Arize). Capture:

Prompt, completion, token counts, latency, cost per request.
Retrieval trace (query, documents, scores).
User feedback signals (thumbs, edits, conversions).

8. Cost controls from day one

Per-user and per-tenant rate limits.
Semantic caching for repeated queries (Redis + embeddings).
Cap max_tokens generously but firmly.
Daily cost dashboard with alerts, not surprise bills.

9. Latency engineering

Stream tokens to the UI so perceived latency drops.
Parallelise retrieval, reranking, and safety checks where possible.
Cache embeddings — they rarely change.
Measure P50, P95, and P99 separately. P99 is where you lose users.

10. Safety & privacy

Know whether your chosen provider trains on your data. (Most serious ones don’t by default — verify.)
If regulated, consider on-prem inference (vLLM, TGI) or enterprise tiers with BAAs/DPAs.
Log redacted inputs only.

11. Rollout strategy

Shadow mode first — run the LLM in parallel with the existing flow, measure agreement.
Small canary (1–5% of traffic).
Gradual ramp with kill-switch.
Feature flag per tenant for enterprise clients.

12. Continuous improvement loop

Collect production samples that users flag.
Add them to your eval set with correct labels.
Re-run evals when prompts, models, or retrieval changes.
Ship monthly, measure, repeat.

Ship-day checklist (TL;DR)

One-paragraph feature spec agreed.
Eval set ≥ 50 cases, automated in CI.
Structured outputs + validator.
Retrieval logged, reviewed, versioned.
Input + output guardrails in place.
Tracing + cost dashboards live.
Rate limits and caching on.
Shadow or canary rollout plan.
Kill-switch tested.
On-call runbook written.

Want help?

Our AI Engineering services cover every item on this list — as a one-off audit, a build sprint, or a continuous retainer. Get in touch and we’ll take an honest look at your setup.