In this post, DCL developers curated a selection of insightful articles and resources that provide a general overview AND delve deeper into RAG technology and the role structured content plays in ensuring accurate, efficient, and reliable interactions with LLMs.
Introducing RAG 2.0
Large language models (LLMs) struggle with knowledge-intensive tasks because they are limited by the information they have been exposed to during training. The RAG approach pretrains LLMs, fine-tunes, and aligns all components as a single integrated system, backpropagating through both the language model and the retriever to maximize performance.
A Beginner's Guide to Building a Retrieval Augmented Generation (RAG) Application From Scratch
Retrieval Augmented Generation, or RAG, is all the rage these days because it introduces some serious capabilities to large language models like OpenAI's GPT-4 - and that's the ability to use and leverage their own data.
This post will teach you the fundamental intuition behind RAG while providing a simple tutorial to help you get started.
Want a More Intelligent Generative AI Chatbot? Start with Intelligent Content!
Radically improving the intelligence of Generative AI chatbots requires leveraging intelligent content, a pivotal aspect often overlooked in early implementations. Michael Iantosca, Senior Director of Content Platforms and Knowledge Engineering at Avalara, presents a compelling case to use DITA to enhance the pre-retrieval accuracy, generation, referencing, and fact-checking of content.
Vector Embeddings in RAG Applications
Vector embeddings are a powerful technique used in machine learning and artificial intelligence to transform raw data into a numerical format that models can easily process. Understanding vector embeddings and the role they play in RAG applications is foundational to building effective RAG systems, enabling precise information retrieval and context-aware responses by grounding AI outputs in relevant and accurate data.
How to Choose the Best Embedding Model for Your LLM Application
Another great introduction to the concepts of RAG and vector embeddings. This article also goes on to explore how businesses can select the best embedding model for their specific use case.
We hope you find these articles useful. Building on the information presented in this collection of recommended reading, Mark Gross is speaking at Text Analytics Forum in Washington DC this month. His presentation, "Genius Without the Gibberish: How RAG and Text Analytics Boost Generative AI Reliability" focuses on how RAG grounds LLM responses against verified data sources. The key components of text analytics—text preprocessing, natural language processing, entity extraction, and structuring data—play a critical role in this process.
Let's us know what your organization is doing with RAG or LLMs by sharing details in the comments. If you want to explore how DCL can support these initiatives, drop us a line!
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