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RAG Evolution – A Primer to Agentic RAG
- Retrieval-Augmented Generation (RAG) is a technique that combines the strengths of large language models (LLMs) with external data retrieval to improve the quality and relevance of generated responses.
- The technique allows LLMs to acquire and analyze data from other available outside sources, particularly helpful in cases where the question is either complex, specific, or based on a given timeframe.
- RAG is evolving, but Traditional RAG Architectures still face several challenges, including summarization, document comparison, structured data analysis, and handling queries with several parts.
- Agentic RAG is the next evolution and uses intelligent agents to answer complicated questions that require careful planning, multi-step reasoning, and the integration of external tools.
- Three primary Agentic strategies include routers, query transformations, and sub-question query engines.
- Agentic RAG and LLMaIndex is a highly demanded backbone by companies willing to leverage AI for enhanced data-driven decision-making.
- Key components of Agentic RAG implementation include tool use and routing, long-term context retention, subquestion engines for planning, reflection and error correction, and complex agentic reasoning.
- Agentic RAG represents a shift in information processing by introducing more intelligence into the agents themselves for achieving a robust and accurate result.
- By being generative and intelligent, these models and Agentic RAGs are on a quest to a higher efficiency as more data is being added to the pipelines.
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