Retrieval-Augmented Generation (RAG) is a new way to build language models. RAG integrates information retrieval directly into the generation process.
In this blog, I will break down how RAG works, why it’s a game-changer for AI applications, and how businesses are using it to create smarter, more reliable systems. What Is RAG? Retrieval Augmented Generation (RAG) is a technique that enhances LLMs by integrating them with external dat...
LLM inputs are limited to the context window of the model: the amount of data it can process without losing context.Chunkinga document into smaller sizes helps ensure that the resulting embeddings will not overwhelm the context window of the LLM in the RAG system. Chunk size is an important ...
You may also want to combine LLM fine-tuning with a RAG system, since fine-tuning helps save prompt tokens, opening up room for adding input context with RAG. Where to fine-tune LLMs in 2025? There are a few different options for where you can fine-tune an LLM in 2025, ranging from...
In short, RAG provides timeliness, context, and accuracy grounded in evidence to generative AI, going beyond what the LLM itself can provide. Retrieval-Augmented Generation vs. Semantic Search RAG isn’t the only technique used to improve the accuracy of LLM-based generative AI. Another technique...
other conversational systems might use RAG to make sure their answers to customers’ questions are based on current information about inventory, the buyer’s preferences, and previous purchases, and to exclude information that is out-of-date or irrelevant to the LLM’s intended operational context....
it still struggles when parsing context across multiple levels of abstraction, resulting in various omissions and errors that are easily spotted by humans. This is why enterprises must proceed cautiously in how they implement these new techniques, whether via vendor tools,foundation modelsor on their...
Relatedly, whatever kind of AI tool you're using, provide it with as much context as you can. With chatbots like ChatGPT and Claude, you can upload documents and other files for the AI to use; with other tools, you can create an entire RAG database for it to pull from. Fact-...
This release introduces major enhancements to boost productivity and reduce repetitive work, including smarter code completion, support for new cloud models like GPT-4.1 (сoming soon), Claude 3.7, and Gemini 2.0, advanced RAG-based context awareness, and a new Edit mode for multi-file edits di...
Outside of the enterprise context, it may seem like LLMs have arrived out of the blue along with new developments ingenerative AI. However, many companies, including IBM, have spent years implementing LLMs at different levels to enhance their naturallanguage understanding (NLU)andnatural language...