Why? and How to Ground a Large Language Models using your Data? (RAG)
Large language models (LLMs) are versatile and can perform a wide range of tasks, but there is a concern that they may not always follow the objectives for which they were created. To tackle this, retrieval-augmented generation (RAG) can be used to provide contextual grounding and offer users a specific set of data for a prompt's response. RAG can work efficiently with vector or pre-existing SQL or No-SQL databases by passing the query response to a large language model, which can then rephrase it into text-based responses that closely resemble human language and structure. The RAG technique reduces the cost associated with other solutions such as fine-tuning, allows for supercharged data retrieval, and delivers better responses with semantic similarity. Using RAG with your data stream can uplift the performance of your recommendation system. The article provides a detailed explanation of RAG with examples and guides to integrate RAG into your system for seamless operation.
Please note that vector databases are commonly used with RAG because they store vector embeddings, which can retrieve semantically matching results, whereas a complete text search strips context off the queries and only matches text. You can use any embedding model to make vector embeddings, and it is adaptive to various data types.
The article offers detailed instructions on how you can implement RAG with vector databases and pre-existing databases and provides additional resources for related topics for further reading.
Published on:
Learn moreRelated posts
What is Retrieval-Augmented Generation (RAG) in Generative AI
This article provides a comprehensive look at how Retrieval-Augmented Generation enhances the capabilities of Large Language Models, ensuring ...
What is RAG, Fine Tuning and RAFT (Retrieval Augmented Fine-Tuning) ?
What is RAG, Fine Tuning and RAFT (Retrieval Augmented Fine-Tuning) ?
What’s a Vector Anyway?
In the world of Artificial Intelligence (AI), vectors are a fundamental mathematical concept that one must understand to utilize Generative AI...
The ABC of AI: Retrieval-Augmented-Generation (RAG) and grounding
In this informative post, the author introduces the concept of Retrieval-Augmented Generation (RAG) and grounding as part of the ABCs of AI. R...
Michael Washington: Azure OpenAI - Episode 286
In this podcast episode, Michael Washington, an experienced ASP.NET and C# programmer, shares insights on process improvement, AI, and Large L...
Armchair Architects: Large Language Models (LLMs) & Vector Databases
David Blank-Edelman and our armchair architects Uli Homann and Eric Charran will be focusing on large language models (LLMs) and vector databa...
Introducing Vector Search Similarity Capabilities in Azure Cache for Redis Enterprise
Created in collaboration with Samuel Partee, Principal Applied AI Engineer, Redis Kyle Teegarden, Senior Product Manager, Azure Cache for Red...
Large language models and copilot in business applications | Dr. Walter Sun
In this video, Dr. Walter Sun highlights the growing impact of large language models (LLMs) on business applications. Through the Azure OpenAI...
Make data more human with Azure OpenAI and Azure SQL
In its bid to bring a more human touch to data, Microsoft announced the general availability of the Azure OpenAI Service (AOAI) in January of ...