Most RAG Hallucinations Are Extraction Errors: Seven Patterns for a Typed Generation Contract
Rejects the "free-text string" generation paradigm in favor of a typed Pydantic object contract that enforces structured output, citations, and self-assessment fields. Distinguishes between true parametric hallucinations and upstream RAG failures (parsing, retrieval, or schema issues), arguing that mislabeling all errors as hallucination prevents effective debugging. Establishes seven generation patterns, including extracting raw typed values rather than performing internal computations to maint
Analysis
TL;DR
- Rejects the "free-text string" generation paradigm in favor of a typed Pydantic object contract that enforces structured output, citations, and self-assessment fields.
- Distinguishes between true parametric hallucinations and upstream RAG failures (parsing, retrieval, or schema issues), arguing that mislabeling all errors as hallucination prevents effective debugging.
- Establishes seven generation patterns, including extracting raw typed values rather than performing internal computations to maintain an auditable trail.
- Replaces single confidence floats with two boolean flags to deterministically route outcomes (off-corpus, partial, complete) based on structural checks rather than model intuition.
- Proposes using programmatic completeness checks at retrieval time (e.g., section boundaries) instead of asking the LLM to judge if its provided context is sufficient.
Why It Matters
This article provides a critical architectural correction for enterprise AI practitioners who rely on naive RAG pipelines. By shifting from unstructured text generation to strict typed contracts, organizations can achieve deterministic validation, full auditability, and clearer error attribution, which are prerequisites for deploying LLMs in regulated or high-stakes environments.
Technical Details
- Typed Contract Architecture: The core proposal replaces free-text outputs with a structured Pydantic schema containing specific fields for values, evidence spans, fidelity flags, and self-assessment metrics.
- Separation of Extraction and Computation: The model is restricted to extracting raw typed values (e.g.,
Amount(value, currency, unit)). All arithmetic, currency conversion, and logical comparisons are handled by downstream Python code to preserve an auditable trail. - Structural Completeness Checks: Instead of relying on the LLM to assess if retrieved chunks are complete, the pipeline uses document structure (e.g., section headers) to determine retrieval bounds, ensuring deterministic coverage.
- Boolean Confidence Flags: The system utilizes two boolean flags to categorize results into three distinct states (off-corpus, partial, complete), enabling precise routing logic compared to ambiguous float-based confidence scores.
- Validation Layer: A validator runs against the typed schema before any data reaches the user, ensuring that every field meets strict type and citation requirements.
Industry Insight
- Shift from "Prompt Engineering" to "Schema Engineering": Teams should invest in defining robust, typed output schemas rather than optimizing prompts for natural language responses, as this enables programmatic validation and integration.
- Auditability as a Compliance Requirement: For enterprise applications, maintaining a clear separation between extraction and computation is essential for regulatory compliance and debugging, as it allows humans to replay and verify every step of the decision process.
- Redefining Error Metrics: Organizations must stop treating all incorrect answers as "hallucinations." Implementing granular error classification (upstream vs. parametric) allows for targeted improvements in parsing, retrieval, or generation components respectively.
Disclaimer: The above content is generated by AI and is for reference only.