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2023/09/17
in RAG
7 min read

RAG is more than just embedding search

With the advent of large language models (LLM), retrieval augmented generation (RAG) has become a hot topic. However throughout the past year of helping startups integrate LLMs into their stack I've noticed that the pattern of taking user queries, embedding them, and directly searching a vector store is effectively demoware.

What is RAG?

Retrieval augmented generation (RAG) is a technique that uses an LLM to generate responses, but uses a search backend to augment the generation. In the past year using text embeddings with a vector databases has been the most popular approach I've seen being socialized.

Simple RAG that embedded the user query and makes a search.

So let's kick things off by examining what I like to call the 'Dumb' RAG Model—a basic setup that's more common than you'd think.

2023/09/11
2 min read

Generating Structured Output / JSON from LLMs

Language models have seen significant growth. Using them effectively often requires complex frameworks. This post discusses how Instructor simplifies this process using Pydantic.