Hybrid Moderation Queue

A content moderation service that routes high-confidence decisions to an LLM and escalates uncertain cases to a lightweight human review dashboard, with latency and accuracy telemetry.

Difficulty: 1-week | Stack: Python, FastAPI, OpenAI API (or Claude), Redis, React, SQLite

Who this is for

Small platform developers (forums, comment sections, indie SaaS) who need moderation that is faster than pure human review but more accountable than a black-box LLM — and want data to tune the confidence threshold over time.

Build steps

1. Define a routing schema: each submission gets an LLM confidence score (0–1) and a policy category (hate, spam, misinformation, safe). Submissions above a high threshold auto-decide; those in the uncertain band enter a Redis queue.
2. Build the FastAPI moderation endpoint: call the LLM with a structured prompt asking for a label + confidence + reasoning snippet; apply the routing logic and persist the decision to SQLite.
3. Build a minimal React reviewer dashboard: shows queued items with LLM reasoning visible, one-click approve/reject, and a running human override rate.
4. Instrument a telemetry layer: track mean latency per route, human override rate by category, and threshold sensitivity (what % of volume hits human review at various cutoffs).
5. Add an A/B threshold experiment: let the operator set two threshold values and randomly split traffic to measure accuracy vs. throughput tradeoff with real data.

Risks

• LLM confidence scores are not well-calibrated — a score of 0.85 may not mean 85% accuracy, so the routing threshold needs empirical calibration before trusting it in production.
• Redis queue can back up faster than human reviewers can drain it during traffic spikes, creating hours-long review latency that defeats the purpose of the hybrid design.
• The LLM reasoning snippet shown to reviewers can anchor their judgment, reducing the independence that makes human review valuable — consider hiding the reasoning as an option.