| name | agentic-rag |
| description | strategies for building Agentic RAG systems. Use this to move beyond static retrieval by using autonomous agents for adaptive source selection, query expansion, and multi-step reasoning. |
Agentic RAG Strategies
Goal
Transform traditional, static Retrieval-Augmented Generation (RAG) into a dynamic, agentic process that actively reasons about how and where to find information.
Core Capabilities
1. Adaptive Retrieval
- Concept: Instead of a single pass against a vector database, the agent dynamically selects the best knowledge source based on the context.
- Mechanism: The agent evaluates the query ambiguity and chooses between multiple data stores (e.g., "PDFs" vs. "Web Search" vs. "Structured DB").
2. Multi-Step Reasoning
- Concept: For complex queries, the agent breaks the problem down into logical steps and retrieves information sequentially.
- Workflow:
- Decompose: Break "Compare the revenue of Company A and Company B" into two sub-queries.
- Retrieve: Fetch revenue for Company A.
- Retrieve: Fetch revenue for Company B.
- Synthesize: Combine both facts into a final answer.
3. Context-Aware Query Expansion
- Concept: The agent doesn't just search for the user's raw query. It generates multiple refined search terms to increase recall.
- Benefit: Captures synonyms, related concepts, and specific terminology that the user might have missed.
Optimization Techniques
- Self-Correction: Implement an Evaluator Agent that reviews retrieved chunks for relevance before generating an answer. If the data is poor, it triggers a new search with better terms.
- Better Search: Enhance the underlying engine with semantic chunking (keeping topics together) and re-ranking (using a second model to order results by quality).
