Traditional RAG vs. Agentic RAG
Understanding the evolution of retrieval-augmented generation systems
What is RAG?
Retrieval-Augmented Generation (RAG) combines information retrieval with text generation, allowing AI systems to access external knowledge sources when formulating responses.
Traditional RAG
A single-step process where the system retrieves relevant documents and generates a response in one pass.
Agentic RAG
An iterative process where the system can refine its retrieval and generation through multiple reasoning steps.
Key Differences
| Feature | Traditional RAG | Agentic RAG |
|---|---|---|
| Process | Single-pass retrieval and generation | Iterative refinement with multiple steps |
| Reasoning | Limited reasoning capabilities | Advanced reasoning and planning |
| Adaptability | Fixed retrieval strategy | Dynamic retrieval based on intermediate results |
| Complexity Handling | Struggles with multi-faceted queries | Better at decomposing complex questions |
| Performance | Faster but potentially less accurate | Slower but more precise and comprehensive |
When to Use Each Approach
Traditional RAG is ideal when:
- You need fast responses with low latency
- The queries are straightforward and well-defined
- Computational resources are limited
- The knowledge base is highly relevant and well-structured
- Simple fact-based answers are sufficient
Agentic RAG shines when:
- Dealing with complex, multi-part questions
- Higher accuracy and completeness are critical
- The problem requires reasoning and planning
- You need to synthesize information from multiple sources
- The query requires iterative refinement
Interactive Comparison
Traditional RAG
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Agentic RAG
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