BENCHMARKSLost in the middle: why long-context models still forget, and how to test it
Needle tests are easy to pass. Real documents need several facts combined from different places. Here is how to measure long-context recall properly.
TESTSRAG chunking strategies compared: fixed, recursive, structural, semantic and parent-child
How you split documents decides what your retriever can find. Five strategies, their trade-offs, sensible defaults and a method to choose on your own data.
MODELSContext windows are not memory
A bigger window lets a model read more at once. It does not make it remember, prioritise or forget well. How to design for that difference.
PROMPTSSystem prompt templates for production: six copy-ready examples
Support, extraction, grounded Q&A, code review, summaries and classification. Templates you can adapt, with the failure each line prevents.
PROMPTSPrompting for citations that actually check out
Source IDs, quote-then-answer, automatic quote checks and a real "not found" path. How to make every claim traceable and verify it in code.
AGENTSMemory for AI agents: what to store, what to forget
Working, episodic, semantic and procedural memory, and the rules for writing, retrieving, updating and deleting each without creeping users out.