📄️ [01] RAG: Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) is an approach that allows LLMs to tap into a large corpus of knowledge from sources and query its knowledge store to find relevant passages/content and produce a well-refined response.
📄️ [02] Multi-Hop Question Answering
A single search query is often not enough for complex QA tasks. For instance, an example within HotPotQA includes a question about the birth city of the writer of "Right Back At It Again". A search query often identifies the author correctly as "Jeremy McKinnon", but lacks the capability to compose the intended answer in determining when he was born.
📄️ [03] Summarization
Summarization is a fundamental task in natural language processing that involves condensing a longer piece of text into a shorter version while retaining its key information and main ideas. It's a crucial skill for both humans and machines, with applications ranging from creating article abstracts to generating concise reports from lengthy documents.
📄️ Community Examples
The DSPy team believes complexity has to be justified. We take this seriously: we never release a complex tutorial (above) or example (below) unless we can demonstrate empirically that this complexity has generally led to improved quality or cost. This kind of rule is rarely enforced by other frameworks or docs, but you can count on it in DSPy examples.
📄️ Additional Resources
Tutorials