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

2026-07-23 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

A new analysis challenges the common understanding of RAG "hallucinations," asserting most are upstream extraction errors rather than LLM fabrications from the model's parametric memory. It proposes a "typed generation contract" framework, where the Large Language Model (LLM) acts as a function filling a Pydantic object, not an oracle returning a simple string. This framework introduces seven patterns to prevent these errors: treating the LLM as a function returning structured data, extracting typed values for external computation, ensuring completeness via document structure, using two boolean flags ("answer_found," "complete_answer_found") instead of a single confidence score, dispatching specific prompts per answer shape, avoiding reasoning models for simple JSON extraction, and decomposing complex schemas for smaller models while allowing single calls for larger ones. This approach emphasizes auditable, verifiable answers with citations and self-assessment fields.

Key takeaway

For MLOps Engineers building RAG systems, stop treating LLM outputs as opaque strings. Implement a typed generation contract using Pydantic schemas to explicitly define answer shapes, citations, and self-assessment flags. This approach transforms "hallucinations" into traceable extraction errors, enabling precise debugging and significantly improving answer reliability. You should decompose complex generation tasks for smaller models to prevent fabricated derived values.

Key insights

RAG "hallucinations" are often extraction errors; fix them with a typed generation contract and structured validation.

Principles

• LLMs are functions, not oracles.
• Separate extraction from computation.
• Validate answers against document structure.

Method

Implement a typed generation contract using Pydantic objects. Dispatch prompts based on desired answer shape. Validate outputs with citations, fidelity flags, and self-assessment fields before user display. Decompose complex tasks for smaller models.

In practice

• Define Pydantic schemas for RAG answers.
• Use Python for numerical comparisons.
• Implement prompt dispatchers for answer shapes.

Topics

• RAG Hallucinations
• Typed Generation Contract
• Pydantic Schemas
• LLM Validation
• Prompt Dispatching
• Enterprise Document Intelligence

Code references

• doc-intel/notebooks-vol1

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

Related on AIssential

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• Stop Returning Text from RAG: The Typed Answer Contract That Prevents Hallucination — Structured output contracts, enforced by constrained decoding, prevent RAG hallucination and enable pipeline control.
• StructHallu-Drift: Benchmarking Structured Hallucinations Under Schema Evolution in LLMs — LLMs frequently produce semantic hallucinations in structured outputs, especially with schema evolution, requiring model-specific mitigation.
• SafeLLM: Extraction as a Hallucination-Resistant Alternative to Rewriting in Safety-Critical Settings — Extraction, especially line-number-based, reduces LLM hallucinations in RAG for safety-critical documents better than rewriting or safety-focused filtering.
• RAG Doesn’t Kill Hallucinations. — RAG transforms hallucinations into confidently cited errors; robust architectural defenses are essential.
• Retrieval-Augmented Generation (RAG): How Modern AI Systems Find the Right Answers Instead of… — RAG empowers LLMs to provide accurate, current answers by retrieving external knowledge rather than relying solely on internal training.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.