Why this matters
AI products do not improve by accidentāthey improve by design. A feedback data collection loop turns real user signals into better models, safer outputs, and higher business impact. As an AI Product Manager, you will:
- Define what feedback to capture (thumbs up/down, corrections, task outcomes, dwell time).
- Instrument the product to collect it reliably and respectfully (privacy, consent, rate limits).
- Turn raw feedback into training/evaluation datasets via labeling and rubrics.
- Prioritize issues, ship fixes, and verify improvements with offline metrics and online experiments.
- Monitor regressions and keep a ānever breakā dataset of critical cases.
Concept explained simply
A feedback loop is a repeatable system that captures signals from users and product behavior, converts them into structured data, and uses that data to evaluate and improve the AI. Think of it as a conveyor belt from āexperienceā to āevidenceā to āenhancement.ā
Mental model
Use the thermostat mental model: you set a target (quality bar), measure the current temperature (user signals + evaluation), and adjust the system (training, prompts, guardrails) to reduce the gapācontinuously.
Signals to consider
- Explicit: ratings, thumbs up/down, user edits/corrections, reason for dissatisfaction.
- Implicit: clicks, dwell time, abandonment, escalation to human support.
- Automated: rule violations, toxicity flags, hallucination detectors, unit tests on prompts.
- Business: conversion, resolution rate, cost per task, time to success.
Core loop components
Write clear quality goals (e.g., āAgent must resolve ā„80% of Tierā1 requests with <2 back-and-forthsā).
Design a consistent data schema for prompts, outputs, user actions, labels, and metadata (locale, segment, model version).
Add UI controls, capture implicit events, and log model I/O with privacy safeguards and consent.
Standardize reasons (e.g., incorrect facts, tone, latency) to turn free text into usable labels.
Decide which items get reviewed (random %, error-prone intents, high-value customers, red flags).
Create a rubric so reviewers apply consistent criteria; include examples and edge cases.
Build golden sets (high-quality), red-flag sets (never regress), and fresh daily samples.
Offline metrics (accuracy, BLEU/ROUGE as applicable), synthetic tests, and human QA; online A/B for impact.
Apply fixes: prompt changes, post-processing, retrieval updates, or fine-tuning; track cost/benefit.
Dashboards and alerts on quality and safety; rollbacks if regressions exceed guardrails.
Example event schema (simplified)
{
"event_id": "uuid",
"timestamp": "ISO-8601",
"user_segment": "free|pro|enterprise",
"task_type": "summarization|qa|recommendation",
"input": {"text": "...", "context_refs": ["doc_123"]},
"model_version": "v1.8.2",
"output": {"text": "...", "latency_ms": 820},
"explicit_feedback": {"thumb": "up|down|null", "reason_codes": ["factual_error"], "user_edit": "..."},
"implicit_feedback": {"dwell_ms": 15400, "abandoned": false},
"automated_checks": {"toxicity": 0.02, "policy_violation": false},
"label": {"grade": "pass|fail|borderline", "rubric_notes": "..."}
}Worked examples
Example 1 ā Document summarizer in a knowledge base
- Signals: thumbs up/down, user-edited summaries, time to first useful sentence, policy flags.
- Loop: Downvotes trigger a required reason; edited summaries are captured as target outputs after PII scrub.
- Improve: Update retrieval snippets, adjust prompt to cite sources, add a hallucination unit test.
- Evaluate: Offline with a small golden set (human-written summaries) and online via resolution rate.
Example 2 ā Product recommendations
- Signals: clicks, add-to-cart, purchase (delayed), hide item, dwell time.
- Loop: Correct for position bias (log propensities), sample a portion for human QA on relevance.
- Improve: Re-rank using a learning-to-rank model trained on debiased feedback.
- Evaluate: CTR uplift in A/B, diversity metrics, and fairness checks across segments.
Example 3 ā Customer support LLM agent
- Signals: resolution rate, escalation to human, CSAT, policy violation triggers.
- Loop: Human-in-the-loop labels via escalation review; red-flag dataset for risky intents (billing, cancellations).
- Improve: Guardrail prompts, stricter retrieval filters, and fine-tuned refusal behavior.
- Evaluate: Reduce escalations while keeping policy violations below threshold.
Example 4 ā Speech-to-text correction
- Signals: user-corrected transcript segments, word-level confidence, noise level metadata.
- Loop: Prioritize low-confidence, high-noise samples for labeling; store before/after edits.
- Improve: Acoustic model fine-tuning on hard segments; add domain-specific vocabulary.
- Evaluate: WER on golden sets by environment (office, car, outdoors).
Designing your loop (quick guide)
- Pick one top metric: e.g., ācorrect answer rateā or āfirst-contact resolution.ā
- Define 5ā8 reason codes: Make them mutually exclusive when possible.
- Instrument: Add minimal UI controls for explicit feedback; log prompts/outputs and key timings.
- Create a rubric: One-page guide with pass/fail criteria and examples.
- Start small: Label 100ā300 samples to build your first golden set.
- Ship a safe improvement: Try a prompt change or retrieval tweak before training.
- Measure: Compare pre/post on golden set and run a small A/B test if traffic allows.
- Automate the boring parts: Daily sampling, dashboards, and alerts on regressions.
Checklist ā ready to run?
- ā Success metric and threshold defined
- ā Event schema documented
- ā Consent and privacy reviewed
- ā Feedback UI and logging live
- ā Rubric + labeling instructions ready
- ā Golden + red-flag datasets created
- ā Evaluation plan (offline + online) written
- ā Rollback plan defined
Exercises
These exercises are available to everyone. Progress is saved only for logged-in users.
Pick a single task your AI performs and create a 1-page feedback loop blueprint.
What to produce
- Goal metric + target
- Event schema fields (10ā15)
- 3 explicit and 3 implicit signals
- 5 reason codes + short descriptions
- Sampling plan (who/when/how much)
- Labeling rubric (pass/fail + 3 examples)
- Evaluation plan (offline + online)
- First improvement you will try
Quality checklist
- ā Reason codes are specific (e.g., āmissing sourceā vs. ābadā)
- ā Sampling includes both successes and failures
- ā Privacy-sensitive fields are minimized or masked
- ā Evaluation links to the goal metric
- ā Rollback criteria are explicit
Common mistakes and self-check
- Collecting everything: Noisy data slows learning. Self-check: Can you list the top 10 fields you actually use?
- Vague reason codes: āBad answerā is not actionable. Self-check: Would two reviewers agree on the code?
- No golden set: Without it, you canāt detect regressions fast. Self-check: Do you have a 100ā300 sample set you trust?
- Skipping consent/privacy: Risky and can block scaling. Self-check: Is PII masked and user consent recorded?
- Overfitting to recent bugs: Balance red-flag sets with representative samples.
- Improving without measuring: Always compare pre/post on stable datasets and monitor online impact.
Practical projects
- Build a 150-sample golden set with a clear rubric and inter-rater agreement notes.
- Create a feedback taxonomy: 8 reason codes with examples and counter-examples.
- Ship a prompt or retrieval update driven by feedback; report pre/post metrics.
- Design a red-flag dataset of 50 āmust not failā cases and add it to CI-style checks.
Who this is for, prerequisites, learning path
Who this is for
- AI Product Managers and aspiring PMs working with LLMs, search, recommendations, or agents.
- Founders and PMs launching early AI features and needing reliable improvement cycles.
Prerequisites
- Basic understanding of model inputs/outputs and evaluation metrics.
- Comfort with simple data schemas and event logging concepts.
Learning path
- Before: Problem framing and success metrics for AI.
- This: Feedback data collection loops.
- After: Labeling operations, golden/red-flag datasets, offline/online evaluation, and release/rollback strategies.
Quick Test
Take the short quiz below to check your understanding. Available to everyone; only logged-in users get saved progress.
Mini challenge
Pick one failure mode from your product (e.g., hallucination on pricing) and write a 5-line plan: the signal, reason code, sampling rule, evaluation metric, and one safe fix to test this week.
Next steps
- Run a 2-week pilot of your feedback loop on a single feature and publish a one-page results summary.
- Automate daily sampling and a simple dashboard for your top metric plus reason code distribution.
- Expand your golden set monthly and keep the red-flag set in every pre-release check.