Most RAG Hallucinations Are Extraction Errors: Seven Patterns for a Typed Generation Contract

Editor's take

Retrieval-Augmented Generation (RAG) systems are exhibiting a specific type of error where the model misinterprets or incorrectly extracts information from provided context, rather than fabricating it entirely.

This distinction is crucial for enterprise AI deployments relying on RAG for factual accuracy, such as in legal document review or customer service bots. Identifying these as "extraction errors" allows for more targeted debugging and robust system design, moving beyond the broader, less actionable term "hallucination." The proposed "typed generation contract" patterns offer a structured approach to mitigating these specific failures.

Future developments will focus on the practical implementation and scalability of these seven patterns within diverse RAG architectures, particularly for complex, multi-document interrogations. Observational data on the reduction of these specific extraction errors in production environments, rather than just benchmark tests, will be key to assessing the efficacy of this methodology.