LangChain is awesome. For people not sure what it's doing, large language models (LLMs) are very powerful but they're very general. As a common example for this limitation, imagine you want your LLM to answer questions over a large corpus.

You can't pass the entire corpus into the prompt. So you might: - preprocess the corpus by iterating over documents, splitting them into chunks, and summarizing them - embed those chunks/summaries in some vector space - when you get a question, search your vector space for similar chunks - pass those chunks to the LLM in the prompt, along with your question

This ends up being a very common pattern, where you need to do some preprocessing of some information, some real-time collecting of pieces, and then an interaction with the LLM (in some cases, you might go back and forth with the LLM). For instance, code and semantic search follows a similar pattern (preprocess -> embed -> nearest-neighbors at query time -> LLM).

Langchain provides a great abstraction for composing these pieces. IMO, this sort of "prompt plumbing" is far more important than all the slick (but somewhat gimicky) "prompt engineering" examples we see.

I suspect this will get more important as the LLMs become more powerful and more integrated, requiring more data to be provided at prompt time.