Feedback Loops and RLHF Self-Hosted

For production AI features with users, capturing feedback (thumbs up/down, explicit corrections, edit-distance from generated to final) creates a continuous improvement loop. Self-hosted lets you actually use this feedback for model improvement — impossible with hosted-API.

The loop

1. User receives AI response; rates it / edits it / accepts it
2. Feedback logged: prompt, AI response, human action (thumbs/rating/edit), final accepted version
3. Periodic batch: convert feedback to DPO preference pairs
4. DPO training run on accumulated pairs (~£10-50 per training cycle)
5. Eval harness: new model meets / exceeds baseline on held-out test set
6. Blue-green rollout to production
7. Loop continues

Feedback data

Three feedback types map to DPO pairs:

• Thumbs up vs thumbs down on similar prompts: chosen = upvoted, rejected = downvoted
• Edit distance: chosen = human-final, rejected = AI-original. Higher quality signal.
• Explicit A/B: show user two variants; chose tracked. Cleanest data.

Aim for 1K-10K preference pairs per training cycle. More is better but with diminishing returns above 10K.

Training

Use HuggingFace TRL DPOTrainer. Workflow:

1. Pull preference pairs from your feedback database
2. Format as DPO training data (prompt, chosen, rejected)
3. Train DPO LoRA over base model (~6 hours on 4090 for ~5K pairs)
4. Eval harness against held-out preference test set
5. Compare to current production model on production-like prompts
6. Promote if eval holds + qualitative review passes

Verdict

For production AI with users, feedback → DPO is the right alignment loop. Self-hosted is the only architecture where this works end-to-end — hosted APIs can't consume your DPO. Quality compounds: each cycle aligns the model better with your users' preferences.