Beyond chatbots: shipping LLM features that survive production
A practical guide to production LLM features: evaluation, privacy-aware tracing, fallbacks, cost control and clear operational ownership.
Why most LLM demos break on contact with users
A demo is tested by the people who built it, on the inputs they expected. Production users bring ambiguous questions, missing context, other languages, adversarial phrasing and documents nobody has seen. The model does not get worse — the input distribution simply gets wider.
The second reason is invisibility. Traditional software fails loudly with an exception; an LLM fails quietly with a fluent, confident and wrong answer. Without deliberate measurement, a team only learns about regressions from complaints.
Start with an evaluation set, not a prompt
Before tuning prompts, we collect 100–300 real or realistic inputs with expected behaviour written by domain experts. This set becomes the contract for the feature. Every prompt, model or retrieval change is run against it, and a change that lowers the score does not ship.
- Cover the boring majority and the dangerous minority (edge cases, refusals, regulated topics).
- Score with a mix of exact checks, rubric-based LLM judges and periodic human review.
- Version the set alongside the code so results are comparable over time.
Trace every call
Record request identifiers, prompt and model versions, latency, token usage, outcome and cost where available. Retrieved context and model outputs may contain personal or confidential information: redact sensitive fields, restrict access and set retention limits before enabling payload capture. Approved, sampled traces can then inform the evaluation set.
Design fallbacks before you need them
Models time out, providers rate-limit and outputs fail validation. Production features need a defined behaviour for each case: retry with a smaller model, return a structured 'I don't know', route to a human queue, or fall back to a deterministic rule. Structured outputs validated against a schema remove an entire class of failures.
Budget cost and latency per request
Set a target cost and p95 latency per interaction from day one. Caching, prompt compression, smaller models for classification steps and batching usually bring costs down substantially without measurable quality loss — but only if the evaluation set proves it.
A release checklist that separates quality from availability
An endpoint can be healthy while its answers are wrong. Set separate release checks for task quality, operational reliability and safe handling of data. Agree thresholds for your workflow rather than borrowing a universal pass rate.
- Keep test data separate from examples used to tune prompts.
- Include injection attempts, missing context and unsupported requests.
- Review errors by severity; an average score can conceal a dangerous failure.
| Check | Evidence to review | Action on failure |
|---|---|---|
| Task quality | Held-out cases, critical-error counts and domain review | Block the release or narrow its scope |
| Service reliability | Timeouts, latency distribution and fallback behaviour under load | Fix capacity or activate a tested fallback |
| Data protection | Redaction, access permissions and retention checks | Disable unsafe capture and investigate exposure |
| Rollback readiness | Versioned configuration and a rehearsed rollback | Keep the previous validated version available |
The operating rituals that matter
A weekly quality review of sampled traces, a named owner for each LLM feature, a changelog of prompt and model versions, and a clear rollback path. These habits, more than any framework, separate a prototype from a product.
Working on something similar?
Discuss your workflow, available data and operating constraints with Talee’s engineers to assess a suitable next step.
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