Stop Returning Text from RAG: The Typed Answer Contract That Prevents Hallucination

2026-07-04 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, extended

Summary

This article, part of the "Enterprise Document Intelligence" series, introduces the "contract" component of the RAG generation brick, focusing on a typed answer schema to prevent large language model hallucination. It details a rich contract that extends beyond a simple answer, incorporating four key field types. These include typed values like Amount and DateValue, multi-element answers with multi-span citations, and self-assessment fields such as confidence, caveats, and answer_found. Additionally, it outlines pipeline-feedback fields like context_structured and conflicting_evidence, alongside a pipeline-computed programmatic completeness signal. The schema, enforced via constrained decoding using tools like Pydantic and OpenAI's responses.parse, ensures structured, auditable outputs with explicit citations and feedback for subsequent pipeline actions.

Key takeaway

For AI Engineers building enterprise RAG systems, you should implement a robust, typed answer contract using constrained decoding. This approach, which employs Pydantic schemas and pipeline-feedback fields, directly combats hallucination by forcing LLMs to return structured, auditable data grounded in retrieved passages. This ensures programmatic utility and enables your pipeline to make informed decisions, such as triggering broader retrieval or seeking clarification, rather than silently propagating incorrect or incomplete answers.

Key insights

Structured output contracts, enforced by constrained decoding, prevent RAG hallucination and enable pipeline control.

Principles

• LLMs hallucinate; shrink the room for invention.
• Ground answers in retrieved passages.
• Never delegate computation to LLMs.

Method

Define a multi-layered typed answer schema (Value, Item, Answer) incorporating self-assessment and pipeline-feedback fields. Enforce this contract via constrained decoding (e.g., Pydantic + responses.parse) for structured, auditable RAG outputs.

In practice

• Use Pydantic schemas for typed RAG outputs.
• Implement multi-span citations for evidence.
• Add pipeline-feedback fields for triage.

Topics

• Retrieval-Augmented Generation
• Hallucination Prevention
• Structured Output
• Constrained Decoding
• Pydantic Schemas
• Pipeline Feedback

Code references

• doc-intel/notebooks-vol1

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

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• Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version) — Using a lightweight graph structure in RAG systems significantly reduces LLM hallucinations and improves factual correctness.
• Fast and Faithful: Real-Time Verification for Long-Document Retrieval-Augmented Generation Systems — Full-context verification for RAG systems significantly improves hallucination detection in long documents under real-time constraints.
• How to Teach the LLM to Think With Your Data — Feeding RAG-retrieved context into an LLM enables reasoning, reducing hallucinations and producing more natural, coherent answers.
• When RAG Users Ask Vague Questions: Clarify Once, Learn the Default — RAG systems can learn from user clarifications to silently resolve vague questions, improving efficiency.

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