How to Ship Your First Production LLM Feature in a Weekend

Most LLM prototypes die in the gap between "it worked once in a notebook" and "real users are hitting it." This guide walks through a weekend-sized plan to close that gap without a full platform team.

Saturday morning: scope and guardrails

Pick one job-to-be-done and one success metric. If you can't write it in one sentence ("summarize support tickets into a 3-bullet brief"), you're not ready to ship.

Then write down the failure modes you refuse to allow in production:

• wrong tenant seeing another tenant's data
• hallucinated facts in a customer-facing reply
• unbounded token spend per request

Each failure mode becomes a guardrail you'll wire in before launch.

Saturday afternoon: build the thin slice

Keep the architecture boring:

1. One model call with a clear system prompt.
2. One retrieval step if you need grounding — a single index, no reranker yet.
3. One output validator (JSON schema or regex) with a single retry.
4. One hard timeout and one max-token cap per request.

Resist agent loops, tool chains, and fine-tuning on day one. You can add them once you see real traffic.

Sunday morning: evals before launch

Write 20–30 golden examples by hand. For each, record the input, the ideal output, and what counts as "good enough." Run them against your prompt and fix the obvious gaps. This is your regression set forever.

Add five adversarial inputs — empty strings, prompt injections, off-topic questions — and confirm the system fails safely instead of producing junk.

Sunday afternoon: launch checklist

• Structured logs with request ID, tenant ID, model, tokens in/out, cost.
• A kill switch (feature flag) you can flip without a deploy.
• A daily cost alert at 2× your expected spend.
• A rollback plan: previous prompt version pinned and ready.
• A 5% rollout to start, with a clear metric you'll watch.

Ship to a small cohort, watch the logs for an hour, then go to dinner. If you'd rather skip the plumbing entirely and ship a tailored assistant on your own sources, SNAP AI — no-code Agent Maker handles the engineering for you.