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
12 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.
Models hallucinate because they are built to sound coherent, not to be right. Here are three prompt-level moves that reduce made-up answers, plus where prompting stops working.
Graph prompting feeds a model structure, not just prose: entities as nodes, relationships as edges. Here is what the idea is, where it comes from, and when it pays off.
For questions that need world knowledge, having the model generate relevant facts first, then answer using them, beats answering cold. How it works, and when to use it over RAG.
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.
If you can already ship software, the way into AI engineering is not another tutorial. It is building five systems that force you to handle failure, measure quality, and run things in production.
Prompt caching can cut input cost by up to 90%, but only if you structure prompts so the cache actually hits. Here is how it works and when it pays off.
A practical, opinionated comparison of the four vector databases people actually argue about, with a clear default and the signals that mean you have outgrown it.
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.