In a recent post on Towards Data Science, a developer reveals a lightweight layer that shields Retrieval‑Augmented Generation (RAG) systems from hallucinations before they reach users. The solution, dubbed a self‑healing module, plugs gaps that appear when an LLM contradicts the document it just fetched.

Typical RAG tutorials end at document retrieval and prompt stuffing, yet the author found the model still issued confident, fact‑free answers—numeric contradictions, fake citations, negation flips, answer drift, and ungrounded confidence. These five failure patterns surfaced in 70 unit tests, each with a named assertion that fully maps to real production failures in deployment cycles.

The architecture follows a simple pipeline: retrieve, generate, inspect, score, heal, route. Inspection runs under 50 ms on a CPU using spaCy, with a regex fallback under 10 ms, keeping latency low while the ConfidenceScorer flags overconfident phrasing and the FaithfulnessScorer checks claim grounding against the source for every query and ensures trustworthy outputs before user consumption.

Deploying the module inside FastAPI adds no external APIs or LLM judges, preserving the system’s simplicity. With 70 passing tests covering every documented failure mode, the author demonstrates that a tiny, in‑process checker can keep RAG answers accurate and reliable, turning an otherwise opaque hallucination problem into a manageable safeguard for developers and product owners.