Generative AI Meets Data Streaming (Part II) – Enhancing Generative AI: Adding Context with RAG and VectorDBs

The text discusses the challenges of using large language models (LLMs) in chatbots, particularly when it comes to providing context-aware responses. The authors introduce retrieval-augmented generation (RAG) and vector databases (VectorDBs), which can enhance LLM performance by providing relevant context. RAG pairs prompts with external data to improve LLM responses, while VectorDBs enable semantic search in unstructured data. The text highlights the importance of selecting the right embedding model for VectorDB implementation and demonstrates how VectorDBs can provide meaningful context, improving the performance of large language models. The authors also mention the need for data streaming platforms and event-driven architecture to unlock true real-time capabilities and scale AI solutions across an organization.

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