Build RAG from Scratch

Large language models have limitations such as not knowing up-to-date data due to training cutoff dates and not having access to private information.

Retrieval-augmented generation (RAG) is a method that provides large language models with additional context from up-to-date or private data to improve their responses.

RAG improves performance by retrieving relevant data to provide context, allowing models to generate responses with information not available at their training cutoff date.

Vector embeddings are lists of numbers that represent the meaning of a body of text, used to capture and compare meaning in natural language processing.

Cosine similarity measures how similar two vectors are, which helps compare the meanings of different texts or queries to improve search relevance.

Tools and technologies for building RAG systems include embedding models, vector databases like AstroDB, and machine learning techniques for natural language processing.

Vector databases enhance RAG systems by efficiently storing and indexing vector embeddings, enabling fast and scalable similarity searches.

Phil Nash is a Developer Relations Engineer at Datastacks, and he is known online as Fonash.

Phil Nash's talk is about building retrieval-augmented generation (RAG) from scratch, particularly in the context of using generative AI models.