Jun 26 • General Discussion 💬
Agentic RAG vs RAG
Agentic RAGs: The Future of AI Workflows
Leading AI companies like Glean, Perplexity, and Harvey use sophisticated Agentic RAGs to handle complex enterprise tasks. This powerful combination leverages the strengths of both:
- RAG (Retrieval Augmented Generation):
- AI Agents:
How Agentic RAGs Combine Strengths:
- Smarter Retrieval: AI agents intelligently manage RAG's data retrieval, dynamically selecting sources based on query needs.
- Enhanced Prompts: Agents build on RAG's augmented prompts by adding planning and real-time, tool-based data, feeding a highly refined input to the LLM.
Operational Workflow (Simplified):
- Query In: Agent receives user query.
- Context & Plan: Agent uses memory and plans data retrieval strategy.
- Data Fetch: Tools (like vector search) gather relevant data.
- Prompt Optimize: Agent combines data, query, and prompt, applying reasoning.
- Response Out: LLM generates final, intelligent output.
Agentic RAGs are transforming how AI tackles complex challenges in the tech industry.
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Agentic RAG vs RAG
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