AI Engineering
Learn LLMs, RAG, agents, secure tools, and production AI through practical insurance examples.
What makes a good prompt?
A clear task, trusted constraints, explicitly separated input, expected output, and a defined response when information is missing. For policy answers, I require supporting evidence and forbid inventing unsupported policy conditions.
Zero-shot or few-shot?
I begin with clear zero-shot instructions. If errors concern category boundaries or formatting, I add a few representative reviewed examples and evaluate them on separate cases. Examples should cover ambiguity, not just easy successes.
How do you maintain prompts?
I treat prompts as versioned configuration with an owner, evaluation dataset, release gate, and trace tag. I investigate patterns in failures instead of accumulating conflicting instructions for individual anecdotes. Provider/model upgrades rerun the same…
Adding many examples improved a demo but slowed production. What next?
I measure which examples actually help and remove redundancy. I test a smaller prompt, provider-supported schemas, deterministic preprocessing, or a better model. I optimize latency and cost while preserving held-out quality.
Difficulty is an editorial estimate. Completion means you chose to mark a question as understood.