Continuous improvement¶

A modern LLM product is never done. The most reliable competitive moat is a tight feedback loop: real usage → failure analysis → targeted training data → updated evals → new model → deploy → repeat.

Feedback loop¶

Collect user interactions — request, response, tool calls, latency, user feedback signals.
Filter and privacy-process logs — redact PII, sample, retain on documented schedule.
Identify failures — automatic (errors, low confidence, low ratings) and manual (random sampling, user reports).
Create targeted evals — every recurring failure becomes a permanent eval case.
Generate or label improved data — write or synthesize SFT/preference pairs that fix the failure.
Fine-tune or preference-train — typically a small targeted update, not a from-scratch retrain.
Red team — verify the fix doesn't introduce new vulnerabilities.
A/B test — run shadow or split traffic.
Deploy gradually — canary → percentage rollout.
Monitor — watch for regressions on existing capabilities.

This loop typically runs on a 1-to-4-week cadence at mature shops.

Common improvement data¶

Bad answers corrected by experts — the highest-leverage SFT data you can get.
Failed tool calls — invalid JSON, wrong tool, hallucinated arguments.
User thumbs down — noisy but plentiful.
Escalated safety cases — anything a human reviewer flagged as harmful.
Hallucination reports — user-flagged or RAG-faithfulness-detected factual errors.
Format failures — schema validation logs.
Long-context failures — questions where the answer was in context but the model missed it.
Domain-specific mistakes — caught by domain SMEs reviewing samples.
Ambiguous prompts where clarification was needed — teach the model to ask.

Beware¶

Do not blindly train on user feedback.

Reasons:

Users may be wrong. A thumbs-down on a correct refusal is bad training signal.
Feedback is biased. Engaged users disproportionately complain about edge cases.
Logs contain private data. PII leakage through training is a serious risk.
Malicious users can poison data. If "user prefers this" influences training, attackers will craft inputs to manipulate the model.
Thumbs-up may reward style over correctness. Users like confident, structured, lengthy answers — even when shorter and uncertain would be more honest.

The remedy is a review pipeline: every piece of user-derived training data should pass through some combination of automatic filtering, expert review, and decontamination before it becomes a training signal.

Privacy and retention¶

Document your retention policy. Users (and regulators) need to know what is logged, for how long, and for what purposes.
Provide opt-out. Users should be able to opt their data out of training.
Run PII detection on logs. Redact before storage when possible; redact at retrieval otherwise.
Audit access. Who can read raw logs? Tracked in an audit log itself?
Plan for deletion requests. Deleting a user's data from a trained model is hard; the legally-cleanest answer is "we don't train on their data" rather than "we deleted it after training."

Metrics that matter for continuous improvement¶

Time-to-fix — from a reported failure to a deployed fix. The lower, the more nimble your team.
Regression rate — how often a new model release introduces a regression. The lower, the better your eval coverage.
Eval coverage — what fraction of historical failures are still caught by your current eval suite?
Drift — distributional change in inputs and outputs over time.
Cost per token, cost per request — must be tracked alongside quality, since improvements often add cost.

Practical tips¶

Make the failure → eval → fix loop explicit. Have a system (or a spreadsheet, at minimum) that tracks every reported failure through to its eval case and its fix.
Invest in synthetic data tools. Generating preference pairs that target specific failure modes is a powerful capability that compounds over time.
Don't retrain from scratch each cycle. Targeted SFT or DPO updates on top of the previous best model is faster and safer.
Save every model. When something regresses, you'll want to bisect.
Hold a "what surprised us" review monthly. What did we learn that changes our roadmap?