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:
  1. Decompose: Break "Compare the revenue of Company A and Company B" into two sub-queries.
  2. Retrieve: Fetch revenue for Company A.
  3. Retrieve: Fetch revenue for Company B.
  4. 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).