The Cost of Probabilistic Hallucinations

In mission-critical sectors such as industrial QA/QC inspection, real estate underwriting, and financial compliance, a 2% hallucination rate is unacceptable. Standard LLMs prioritize linguistic fluency over factual accuracy.

The Architecture of Deterministic RAG

Retrieval-Augmented Generation (RAG) decouples the reasoning engine (the LLM) from the knowledge repository (the Vector Database). Instead of generating answers from pre-trained memory, the AI executes a three-stage deterministic loop:

  1. Hybrid Search (BM25 + Dense Embeddings): Parses internal SOPs, contracts, and codebases into localized vector embeddings.
  2. Context Injection: Feeds only verified semantic chunks into the model context window.
  3. Strict Negative Constraint: Model is programmed to output explicit failure tokens if no verifying document exists in the index.

By enforcing source attribution down to the exact page and section number, AXONIC S.R. RAG pipelines guarantee zero-hallucination compliance.