Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
Data Science
Towards Data Science·Aug 7·5 min read
Loop Engineering for Listing Questions: When the Answer Is Every Passage, Not the Top One
TL;DR
A new approach to Retrieval-Augmented Generation (RAG) pipelines enables the listing of all relevant passages in response to questions, rather than just the top answer. This method addresses a critical failure in traditional systems, enhancing document intelligence capabilities.
✦ Why It Matters
Implement Loop Engineering in your RAG pipelines to improve the retrieval of multiple relevant passages for complex queries.
Key Takeaways
- Recognize listing questions using regex or LLM classifiers.
- Implement structure-based retrieval for documents with clear markers.
- Use a two-pass approach for semantic aggregation of items.
- Deduplicate items using both surface and semantic methods.
How It Works
The pipeline identifies listing questions and employs three strategies: structure-based retrieval uses document headings and lists, pattern-based retrieval identifies items through regex matching, and semantic aggregation involves broad retrieval followed by LLM extraction to ensure completeness. This two-pass approach allows for iterative refinement based on LLM feedback.