AI Engineering
Learn LLMs, RAG, agents, secure tools, and production AI through practical insurance examples.
What is an LLM good for?
An LLM processes language using learned patterns and supplied context. I use it to summarize, extract, classify, explain, and propose tool actions. Current claim status and business permissions come from services, not the model's memory.
Does temperature zero guarantee the same correct result?
No. It generally reduces sampling variation where supported, but it does not verify evidence or eliminate all runtime variation. I use schemas, authoritative data, bounded outputs, and evaluations to establish application reliability.
How do you choose a model?
I compare candidate models on representative tasks, including failure and adversarial cases. I measure correctness, supported tool/schema capabilities, latency, cost per successful task, and deployment/data restrictions, then version the selected…
The model invents a claim status. What do you change?
I remove any path that asks it to know account facts. An authorized tool returns canonical status; a template or model explains that result. I test missing claims, denied access, backend failure, and unsupported extra statements.
Difficulty is an editorial estimate. Completion means you chose to mark a question as understood.