Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
Data Science
Towards Data Science·Jul 23·5 min read
Most RAG Hallucinations Are Extraction Errors: Seven Patterns for a Typed Generation Contract
TL;DR
Most errors in Retrieval-Augmented Generation (RAG) models are misidentified as hallucinations when they are actually extraction errors. The article introduces seven patterns for a typed generation contract to improve accuracy.
✦ Why It Matters
Implement typed generation contracts today to reduce extraction errors in your RAG models.
Key Takeaways
- Use structured outputs instead of plain text for better reliability.
- Implement self-assessment fields to track confidence in answers.
- Separate found data from invented information in model outputs.
- Adopt a staged extraction process for smaller models to avoid errors.
How It Works
The approach utilizes a typed Pydantic schema to define the structure of answers, ensuring that each output includes specific fields such as citations and confidence levels. This structured format allows for clear validation and auditing of the information provided by the model, making it easier to identify errors and track the source of data.