RAG

LLMs are being experimented with to do so many things today, and one of the use cases that sound compelling is getting their help to generate insights from data. What if you could find the answer to your data question without begging a data analyst in your company? But this is easier said than done. To perform this task properly, LLMs need to know about your datasets, the tables, their schemas, and values stored in them. You can provide this information in the prompt itself if your dataset is tiny, but this is not possible in most real life scenarios, since the information will be huge and either it won’t fit the LLM’s context knowledge or it will be very expensive and not feasible. ...

If you want to make a good RAG tool that uses your documentation, you should start by making a search engine over those documents that would be good enough for a human to use themselves. This is exactly what I’ve been trying to communicate in my org in the past few months. It’s 2024 and we still can’t have a proper search engine in organizations to find relevant information from various sources. While this problem remains to be solved, organizations are adapting RAG and AI into their tooling, but are missing the important R of the RAG: Retrieval. I’ve been an advocate of prioritizing search engines over any AI related tool in the past few months, and I found it refreshing to read about this somewhere else: ...

In his excellent write up on state of the art embedding models, Aapo Tanskanen compares the retrieval score for when the source documents are split into chunks and when they’re not: Transformer-based single embedding models have traditionally had 512 token context windows because of the usage of the original BERT encoder. Newer models, like the BGE-M3, have expanded the token window to much larger scales. It could be tempting to forget chunking and just embed long texts as they are. However, that would mean mashing many topics and entities you might want to search for into a single vector representation, which doesn’t sound like a good idea. ...