A pre-launch checklist for AI features moving to production

Shipping an AI feature to production is not the same as shipping a CRUD endpoint. The pre-launch checklist for AI features has to account for nondeterminism, provider outages, and silent quality regressions that no unit test will catch. If you treat staging as a toy and production as an afterthought, you will learn about your mistakes from users.

1. Pin model versions and enforce staging parity

Model providers rotate weights under the same marketing name. Your staging environment must run the exact same model snapshot as production, or your eval numbers are lies. Use versioned model IDs in every request instead of aliases like gpt-4o.

{
  "model": "gpt-4o-2024-05-13",
  "messages": [{"role": "user", "content": "Summarize this ticket"}]
}

Store the version in a single config module and fail loudly if staging and production drift. A pre-launch checklist for AI features is worthless if the staging cluster quietly points at a newer model than what you validated.

2. Build an eval harness that runs on real traffic samples

Synthetic prompts hide failure modes. Capture a few thousand representative inputs from staging—or a shadow copy of production—and run them through your pipeline on a schedule. Score outputs with a fixed heuristic or a separate judge model with temperature 0.

def eval_batch(samples, client):
    results = []
    for s in samples:
        resp = client.chat.completions.create(
            model=MODEL, messages=s, temperature=0
        )
        results.append(score(resp.choices[0].message.content, s["expected"]))
    return sum(results) / len(results)

Track the score over time. A dip of even two points on a 100-item set is a signal to block the launch. The pre-launch checklist for AI features should treat eval regression as a build failure, not a footnote.

3. Define fallback and routing before you need it

Providers throttle and go down. Your service should declare a primary and secondary route, and test both paths in staging. An inference gateway like n4n.ai provides one OpenAI-compatible endpoint covering 240+ models with automatic fallback when a provider is rate-limited, which lets you validate a single client config instead of hand-rolling retries.

curl https://api.n4n.ai/v1/chat/completions \
  -H "Authorization: Bearer $KEY" \
  -d '{"model":"auto","messages":[{"role":"user","content":"hi"}]}'

If you roll your own, write the retry logic now. Simulate a 429 with a fault injector and confirm your timeout budget holds. The pre-launch checklist for AI features must include a proof that the app survives a provider outage without hanging the request thread.

4. Set hard token and cost ceilings per request

LLM calls can loop or emit verbosely. Cap max_tokens at the smallest value that fits your use case, and wrap calls in a deadline. Per-token usage metering should be logged on every response so finance does not discover the bug later.

resp = client.chat.completions.create(
    model=MODEL,
    messages=msgs,
    max_tokens=512,
    timeout=8.0,
)
print(resp.usage.total_tokens)

Add a circuit breaker that rejects requests if a user session exceeds a daily token quota. This is not optional for consumer-facing features—one recursive agent mistake can burn a month of budget in an hour.

5. Instrument everything with traces and token counts

You cannot debug what you cannot see. Emit a span for each model call containing the model ID, token counts, latency, and a hash of the prompt template. Correlate it with the parent request ID.

{
  "trace_id": "a1b2",
  "model": "gpt-4o-2024-05-13",
  "prompt_hash": "9f3c",
  "completion_tokens": 120,
  "latency_ms": 840
}

A pre-launch checklist for AI features is incomplete without a dashboard that shows tail latency and error rates per model. If your observability stack treats the LLM as a black box, you will be blind during the first incident.

6. Honor cache-control and rate-limit hints

Many providers support prompt caching. Forward their cache-control hints from your client, and respect Retry-After headers instead of hammering. This reduces cost and keeps you under rate limits.

POST /v1/chat/completions HTTP/1.1
Authorization: Bearer $KEY
Cache-Control: max-age=3600

In staging, verify that repeated identical requests actually hit the cache by checking usage metadata. A launch that ignores caching pays a tax on every repeated system prompt.

7. Validate inputs and assume adversarial prompts

Users will paste instructions to override your system prompt. Treat all external text as untrusted data, not as control signals. Use structured outputs or strict delimiters, and reject malformed inputs before they reach the model.

if "ignore previous instructions" in user_text.lower():
    raise ValueError("injection pattern blocked")

The pre-launch checklist for AI features should include a red-team pass where you attempt to leak the system prompt or exfiltrate PII. If your guardrail is a polite request in the system message, it will fail.

8. Ship behind a canary and keep a rollback path

Do not flip the switch for 100% of traffic on day one. Use a feature flag to route 5% of requests to the new AI path and compare latency, error rate, and eval scores against the old path. Keep the flag reversible in one command.

Stage	Traffic	Rollback trigger
Canary	5%	Error rate > 2% or eval drop > 3%
Half	50%	Tail latency p99 > 2s
Full	100%	Manual approval

A pre-launch checklist for AI features ends with a documented rollback runbook. If the only way to disable the feature is a code deploy, you are not ready.

Synthesis

The difference between staging and production for AI features is not scale, it is uncertainty. Models change, providers fail, and users behave unpredictably. The items above convert that uncertainty into testable contracts: pinned versions, eval gates, fallback routes, hard limits, traces, cache awareness, injection defenses, and canary control. Run through this list with the same rigor you apply to schema migrations, and your launch will be boring—which is exactly what you want.