Retrieval Augmented Generation (RAG): Explained

Retrieval-Augmented Generation (RAG) is a technique in generative AI that enhances Large Language Models (LLMs) by dynamically incorporating external data into the context window, making it valuable for applications requiring up-to-date or domain-specific information. RAG applications typically involve a pipeline where user queries are processed to retrieve relevant data from a knowledge base, which is then integrated into a prompt template for the LLM to generate enriched responses. This approach is cost-effective, allows for quick development, and improves user trust by providing verifiable data sources, making it especially useful in fields like legal, healthcare, and finance. The Humanloop platform offers tools to develop and evaluate RAG systems, facilitating collaboration in AI application development and helping teams efficiently transition from concept to production.

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