Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
Data Science
Towards Data Science·Aug 31·5 min read
Why RAG Complexity Should Be Earned
TL;DR
A framework for building Retrieval-Augmented Generation (RAG) pipelines is proposed, emphasizing the need to introduce complexity only when necessary to address specific failure modes. This approach enhances the effectiveness of search and information retrieval processes.
✦ Why It Matters
Evaluate your current RAG pipeline and consider implementing complexity only where failure modes are evident.
Key Takeaways
- Introduce complexity in RAG only when retrieval failures are evident.
- Evaluate retrieval and generation as separate components.
- Use hybrid retrieval to combine strengths of lexical and semantic methods.
- Ensure retrieval quality to enhance the performance of agentic systems.
How It Works
RAG systems perform two key operations: retrieval, which identifies relevant information, and generation, which constructs responses. By separating these functions, designers can better diagnose failures.
For example, if a query about contractual terms retrieves irrelevant passages, the issue lies in the retrieval phase, not the generation phase. Hybrid retrieval methods combine lexical and semantic approaches to improve candidate selection, ensuring a broader range of relevant documents is considered.