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Articles tagged with #llm
The prompt is only about 10% of what your LLM actually sees. Most of the other 90% is context you assemble — and when your data has structure, you should build it with code, not another LLM call.
I'm building an open-source system to interrogate the AI-research firehose — RAG now, a multi-agent researcher by the end, in public over five parts. Part 1 is the foundation: the architecture, the ingestion, and the eval bug that would have shipped meaningless numbers.
A sub-agent that writes to your system will tell you it succeeded. It's reporting what it intended to do, not what actually landed — and the gap is where silent data corruption lives.
When you build on top of a language model, routing every decision to the model is the path of least resistance. One interface, one prompt, one place to add logic. It works beautifully in a demo. Then
When one LLM generates content and another from the same family reviews it, they can agree completely — and both be wrong in the same direction. That's not a capability failure. It's family bias: a st
