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# RAG is more than just embedding
- URL: https://www.taivo.ai/__rag-is-more-than-just-embedding/
- Published: 2023-09-21T10:31:31.000Z
- Updated: 2026-01-17T19:38:35.000Z
- Author: Taivo Pungas
- Tags: Systems, stream

90% of time when people say "[Retrieval-augmented generation](https://taivo.ai/stream/%5F%5Fretrieval-augmented-generation?ref=taivo.ai)" they mean that the index is built using an **embedding* model like OpenAI's `text-embedding-002` and a **vector database* like Chroma, but it doesn't have to be this way. **Retrieval* is a long-standing problem in computer science -- a couple of PhD students made a breakthrough in the field in the 90s and turned this into Google -- and there are many approaches to it.

A very simple one would be to build an index based on counting keywords, which is what **tf-idf* does with some bells and whistles. Or you could literally use Algolia, or ElasticSearch, or another search/retrieval framework to do RAG. Heck, in some domains, `grep` might be the best retriever possible.

I think the "old" retrieval methods are undervalued and embedding-plus-vector-DB approach is way overused. If I had to guess, most use cases would benefit from some form of hybrid between different methods. It's decades since Google was a single algorithm... so why should your app's retrieval be any different?