Beyond RAG: Building an Autonomous 'Epistemic Engine' to Fight AI Hallucination
Source: Dev.to
The “Yes Man” Problem
If you’ve built a RAG application, you’ve seen it: you ask a leading question with a false premise, and the LLM happily hallucinates evidence to support you. This is called sycophancy, and it’s a silent killer for trust in AI.
Enter FailSafe
The Architecture: Defense in Depth
FailSafe treats verification like a cybersecurity problem. It uses multiple layers of filters to ensure only high‑quality facts survive.
The Statistical Firewall (Layer 0)
Why waste tokens on garbage? We use Shannon entropy and lexical analysis to reject low‑quality inputs instantly. It’s a “zero‑cost early exit” strategy.
Specialized Small Models (SLMs)
We don’t need GPT‑5 for everything. FailSafe offloads tasks like coreference resolution (“He said…”) to specialized models such as FastCoref. This is faster, cheaper, and often more accurate for specific grammatical tasks.
The Council: Managing Cognitive Conflict
This is the core. Instead of a single agent, FailSafe employs a Council of three distinct agents:
- The Logician – Detects formal fallacies in reasoning.
- (Other agents would be listed here in the original source.)
Conclusion
We call FailSafe an “Epistemic Engine” because it prioritizes the integrity of knowledge over conversational fluency. It’s open source, and we’re looking for contributors to help push the boundaries of autonomous verification.