Simple RAG vs Agentic Approach

Overview: I use Ollama to run open source LLM models with various data sources to ask questions and include cited sources. If you would like to try this out on your machine (or try out any of the other examples) you can clone the following github repository: https://github.com/jbsoftware-io/gen-ai-starter

Model Used: gemma3:4b

Tools: Wikipedia

Question: Can you explain agentic approach vs simple RAG when using LLMs? What are the pros and cons of each approach?

Answer:

Here’s a comparison of the agentic approach versus simple RAG when using LLMs:

Retrieval-Augmented Generation (RAG)

• Description: RAG involves providing an LLM with relevant documents or data to augment its pre-existing knowledge. The LLM then uses this retrieved information to generate responses.
• Pros:
  • Simple to implement.
  • Reduces hallucinations by grounding responses in factual data.
  • Effective for answering questions based on a known knowledge base.
• Cons:
  • Limited by the quality and scope of the retrieved documents.
  • Doesn’t actively explore or synthesize information.
  • Can struggle with novel or complex queries that require deeper reasoning.

Agentic Approach

• Description: An agentic approach involves an LLM interacting with external tools (e.g., search engines, calculators, databases) to perform tasks and gather information before generating a response. It’s a more active and dynamic approach.
• Pros:
  • Can access and process information in real-time.
  • Capable of performing complex tasks that require multiple steps.
  • More adaptable to novel situations and can handle ambiguous queries.
  • Can improve accuracy by verifying information through external tools.
• Cons:
  • More complex to implement and manage.
  • Requires careful design of the agent’s behavior and tool selection.
  • Can be vulnerable to errors if the agent misinterprets the query or tool outputs.
  • Potentially higher computational cost due to the interaction with external tools.

In essence, RAG is a passive retrieval method, while the agentic approach is an active, interactive method that leverages external tools to enhance an LLM’s capabilities.