Notes

RAG is conceptually simple

RAG boils down to 5 steps:

1. Create a representation of all the possible information (text) you’d like to be considered for your question (info-representation)
2. Create a representation of the question being asked (question-representation)
3. Find the top N info-representations most similar to your question-representation
4. Feed all of the information (text) from the top N representations into your LLM of choice (e.g., OpenAI GPT4o) along with the question
5. And Voila! Your model will give you an answer given the context you’ve added

It could almost be called “Expand your LLM prompt with more context”.