RAG
Generating a Synthetic Dataset for RAG
When retrieval fails in your domain or language, you can prompt a large model to generate training data for a custom retriever, matching thousands of hand-labels for tens of dollars.
6 min read
Retrieval-augmented generation, from basics to agentic retrieval.
6 articles
When retrieval fails in your domain or language, you can prompt a large model to generate training data for a custom retriever, matching thousands of hand-labels for tens of dollars.
How to build a RAG system that serves many customers without leaking data between them: silo vs pool vs bridge, namespaces vs metadata filters, and where isolation must hold.
Embeddings turn text into vectors so meaning becomes geometry. Here is the mental model, the gotchas, and how to choose a model in 2026.
Rerankers re-score your top candidates with a model that actually reads the query and the document together. Here is how they work and when they pay off.
Chunking quietly decides how good your RAG system can ever be. Here is how the main strategies compare and a simple way to pick one.
Traditional RAG retrieves once and hopes for the best. Agentic RAG lets the model decide whether, what, and how to retrieve, then check its own work. Here is the difference, with code.