Tagged
#retrieval
7 articles
Embeddings vs generation: two things models do
"Embeddings and generation are different jobs. Knowing which one your problem needs is the fastest way to a system that actually works."
Retrieval-augmented generation (RAG), from first principles
RAG is often explained as a stack of tools. Strip that away and it is one simple idea: let the model read the right material before it answers. Here is how it really works.
Choosing an embedding model for your project
Picking an embedding model is less about leaderboards than fit. Here is what actually decides whether retrieval works for your data and your budget.
Why models have knowledge cutoffs
A model's knowledge stops at a date because its knowledge is frozen at training time. Here is why that happens and how tools work around it.
Document Q&A that actually works: patterns and pitfalls
Asking questions over your own documents is the most useful AI demo and one of the easiest to get quietly wrong. Here are the patterns that survive real use.
Chunk documents well for retrieval
Retrieval is only as good as its chunks. Here is how to split documents so the right passage comes back whole and in context.
Build a simple RAG pipeline: a conceptual walkthrough
Retrieval-augmented generation, built up one stage at a time. No magic, no specific stack — just the shape of the pipeline and the decisions that matter.