RAG Hallucinations: Solving Extraction Errors via Typed Contracts
Calling every incorrect answer in a retrieval-augmented generation (RAG) pipeline a "hallucination" is an oversimplified diagnosis. If the model has the accurate context provided in its prompt yet still outputs the wrong value, that error isn't a hallucination, but a simple extraction failure. Distinguishing between these two scenarios is crucial for effectively improving the reliability of your AI workflow.
How to Mitigate Incorrect Answers in RAG Pipelines?
To mitigate these errors, you need to implement a typed generation contract—essentially forcing the language model to adhere to a strict schema instead of allowing it to generate freely. When the model is constrained to produce specific types, like a boolean, date, or predefined enumeration, it greatly reduces the surface area for introducing erroneous "imaginings."
Decomposing Complex Schemas for Smaller Models
For those working with smaller models that struggle with complex schemas, consider a decomposition approach: break the extraction process into multiple smaller, typed steps rather than attempting a single large JSON object generation.
Here are some practical ways to implement this in your pipeline:
Practical Steps to Implement Typed Generation Contracts
- Schema Enforcement: Define your expected output using tools like Pydantic or JSON Schema. This converts the task from open-ended content generation to filling out predefined form fields with structure.
- Type Casting: Require the model to output data in specific formats, such as
YYYY-MM-DD. This prevents it from drifting into conversational or overly flexible responses and keeps the output precise. - Decomposition: If a model fails to accurately extract all five necessary fields in one go, create five separate, targeted calls. This approach adds a bit of latency but significantly boosts accuracy.
Transforming Language Models into Structured Data Extractors
By viewing the language model as a structured data extractor rather than a creative writer, you can transform a fragile demo into a robust production-ready tool. The key shift in prompt engineering is moving from vague instructions like "answer this" to precise directives like "extract these specific types." This focus on type safety will bring true stability to your RAG workflow.
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Pydantic fixed my JSON parsing nightmares last month. Are you using the latest V2? One practical step is to define a strict Pydantic schema for your expected output, which forces the model to fill predefined fields instead of generating freely—this sharply reduces the chance of it inventing erroneous data.
Does this actually work on smaller models or is it just for the heavy hitters? While the typed generation contract approach is powerful for larger models, breaking down complex schemas into smaller, typed steps—like enforcing strict date formats such as YYYY-MM-DD—can directly address extraction failures in smaller models by reducing ambiguity and forcing precise outputs.
Instructor is a lifesaver for schema validation. Which specific version are you running? One concrete step that helped me: define your expected output with Pydantic or JSON Schema so the task becomes filling predefined fields instead of open-ended generation.