Summary

RAG is a rapidly evolving technique that overcomes knowledge limitations in neural generative models by conditioning them on relevant external contexts. We uncovered how training LLMs with a RAG approach works and how to implement RAG with Amazon Bedrock, the LangChain orchestrator, and other GenAI systems. We further explored the importance and limitations of RAG approaches in the GenAI realm. As indicated, early results across a variety of domains are promising and demonstrate the potential of grounding text generation in real-world knowledge. As research addresses current limitations, retrieval augmentation could enable GenAI systems that are factual, informative, and safe.

In the next chapter, we will delve into practical applications by employing various approaches on Amazon Bedrock. We will commence with a text summarization use case, and then explore insights into the methodologies and techniques in depth.