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How agentic RAG can be a game-changer for data processing and retrieval
- Enterprises have run into instances where RAG fails to deliver the expected results, prompting the development of agentic RAG, which incorporates AI agents into the pipeline.
- AI agents with memory and reasoning capabilities enable agentic RAG to retrieve data from multiple knowledge sources, going beyond fetching information from just one knowledge source.
- Agentic RAG can improve downstream LLM applications by enabling them to produce more accurate and validated responses to complex user queries.
- There are two main ways to set up agentic RAG pipelines: incorporating a single agent system; and setting up multi-agent systems with a series of specialized agents that work across their respective sources to retrieve data.
- Agentic RAG is still new and can run into occasional issues, including latencies stemming from multi-step processing and unreliability.
- The agentic RAG pipeline could be expensive as the more requests the LLM agent makes, the higher the computational costs.
- Agentic architectures are critical for the next wave of AI applications that can 'do' tasks rather than just retrieve information.
- Enterprises should explore additional capabilities such as agentic AI and Generative Feedback Loops as they continue to level up their RAG applications.
- The approach expands the knowledge base powering downstream LLM applications, enabling them to produce more accurate, grounded, and validated responses to complex user queries.
- DSPy, LangChain, CrewAI, LlamaIndex, and Letta simplify building agentic RAG systems by plugging pre-built templates together.
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