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How agentic RAG can be a game-changer for data processing and retrieval
Organizations have already started upgrading from vanilla RAG pipelines to agentic RAG, thanks to the wide availability of large language models with function calling capabilities and new agentic frameworks.
They developed LLMs applications using Retrieval-Augmented Generation (RAG), a technique that tapped internal datasets to ensure models provide answers with relevant business context and reduced hallucinations. The approach worked like a charm, leading to the rise of functional chatbots and search products that helped users instantly find the information they needed, be it a specific clause in a policy or questions about an ongoing project. According to the Weaviate team, AI agents can access a wide range of tools – like web search, calculator or a software API (like Slack/Gmail/CRM) – to retrieve data, going beyond fetching information from just one knowledge source.
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