Prompt engineering is mostly over. The job now is context engineering.
The skill that matters is not wording a clever prompt. It is deciding what goes in the context window, what stays out, and in what order.
aillmcontextagents
View →
Blog - llm
How we cut our LLM bill about 30% without making the product worse
Three levers that actually moved our token spend: caching what repeats, routing to the right size of model, and sending less in the first place.
aillmcostengineering
View →
Graph memory and the token tax nobody is paying attention to
Stuffing an LLM's whole memory into every prompt is the most expensive habit in agent engineering. Here is how the memory crowd is fixing it, and the four-way retrieval I keep reaching for.
aimemoryllmrag
View →
Stop vibe-checking your AI. Build the evals.
If the only way you know your AI feature works is that it felt right in the demo, you do not actually know it works. Here is how to start measuring.
aievalsllmtesting
View →