Introduction
This article delves into the challenges faced by LLM, the utilization of Retrieval Augmented Generation (RAG) patterns, the anatomy of RAG, various search types, and strategies for overcoming LLM obstacles.
Problem
Large language models (LLMs) such as ChatGPT are trained on publicly available internet data from the time of their training. They can respond to queries based on this data. However, this public data may not always fulfill your requirements, as you might need answers derived from your private data or because the information might be outdated.
Solution
To address this, Retrieval Augmented Generation (RAG) is used. This AI technique leverages an LLM to generate answers using your specific data.
Retrieval Augmented Generation (RAG) Pattern
Retrieval Augmented Generation (RAG) is a technique that integrates your data with an LLM to produce answers tailored to your specific information. When a user poses a question, the data store is queried based on the input. The user’s question is then merged with the matching results and sent to the LLM using a prompt to generate the desired response.
Anatomy of Retrieval Augmented Generation (RAG)
![RAG]()
RAG generates answers to user questions using your data. For RAG to function effectively, we need a method to search and send your data to the LLMs efficiently and cost-effectively. This is accomplished by using an index, which is a data store that enables efficient data searches. In RAG, an index is particularly useful. It can be optimized for LLMs by creating vectors (converting text data into number sequences using an embedding model). A well-optimized index typically includes efficient search capabilities, such as keyword searches, semantic searches, vector searches, or a combination of these.
Types of search
- Keyword search: Locates relevant documents or passages based on specific keywords or terms provided in the input.
- Semantic search: Finds documents or passages by comprehending the meaning of the query and matching it with semantically related content rather than relying solely on exact keyword matches.
- Vector search: Utilizes mathematical representations of text (vectors) to identify similar documents or passages based on their semantic meaning or context.
- Hybrid search: Integrates multiple search techniques, executing queries in parallel and returning a unified result set.
Best Practices
When creating a search index in Azure AI Studio, you are guided through configuring it to be optimally compatible with a language model. For generative AI applications, hybrid search provides the most accurate results.
Summary
In this article, we successfully learned about the challenges of LLM, the Anatomy of RAG patterns, and the types of searches in Azure AI Studio. By overcoming these challenges, RAG systems can reach their full potential, becoming a powerful tool for improving the accuracy and effectiveness of LLMs in various applications.
I hope you have enjoyed reading this article.
Happy Learning!