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Goodbye Vanilla RAG, Agentic RAG is Here
- Vanilla RAG approach is reaching its limitations, where as agentic RAG is here aligning its future with AI platforms. It helps in delivering adaptable, proactive AI systems using external knowledge sources. Core principle of RAG is combined with flexibility of AI agents and assist in creating dynamic frameworks. It adapts the retrieval strategy as per the user needs and provides proactive and adaptive nature.
- Agentic RAG core objective is to integrate AI agents into the retrieval-augmented generation pipeline. These agents allow for autonomous frameworks, considering multi-step reasoning, planning and tool Utilization. It addresses issues like predefined knowledge sources, validating retrieved data and not providing iterative refinement. Various stages of retrieval are integrated with agent-based systems to provide better adaptive strategies.
- Agentic RAG integrates agents to orchestrate complex tasks involving flexible knowledge validation, departmental data retrieval, access to exclusive APIs/Databases, and multi-document retrieval. Agents are specialized in summarizing internal documents, retrieving public data, or analyzing personal content like chat logs.
- With agentic RAG, it seems like fine-tuning and RAG conversation is finally over. It provides accuracy and speed in resolution by retrieving information from internal knowledge base, documentation, and community forums. The agentic RAG framework combines LLMM layers to reason over inputs and post-process outputs.
- Agentic RAG framework is not limited to single agent systems only, multiple agents collaborate under the guidance of a meta-agent. RAG systems are needed when core AI systems are not accurate enough for information. Google introduced a new approach to move beyond RAG, retrieval interleaved generation (RIG) using LLMs with Data Commons.
- In agentic RAG, agents access varied knowledge sources beyond just databases, anticipate user needs and take preemptive actions, enabling a smoother and more efficient interaction process. It is therefore considered effective in scenarios requiring detailed reasoning, multi document comparison, and comprehensive decision-making.
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