RAGpipeline a "hallucination" is a lazy diagnosis. If the model has the correct context in its prompt but still outputs the wrong value, that's an extraction error. Distinguishing between the two is the only way to actually fix the reliability of your AI workflow.
To stop these errors, you need a typed generation contract—basically forcing the LLM to adhere to a strict schema rather than letting it freestyle. When the model is forced into a specific type (like a boolean, a date, or a predefined enum), the surface area for "imagining" things shrinks significantly.
For those of you working with smaller models that struggle with complex schemas, the best approach is a decomposition rule: break the extraction into multiple smaller, typed steps rather than one giant JSON object. Here are a few ways to implement this as a practical tutorial for your pipeline:
-
Schema Enforcement: Define your output using Pydantic or JSON Schema. This transforms the task from "writing a summary" to "filling a form."
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Type Casting: Force the model to output specific formats (e.g.,
YYYY-MM-DD
) to prevent it from drifting into conversational filler.
- Decomposition: If a model fails to extract five fields accurately, create five separate calls. It's slightly more latency, but the accuracy jump is massive.
By treating the LLM as a structured data extractor rather than a creative writer, you can turn a flaky demo into a production-ready tool. This shift in prompt engineering—moving from "answer this" to "extract these specific types"—is where the real stability happens.
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