Big ideas. Useful next steps.

When an AI feature fails, the final answer rarely explains the whole problem.

Tool calling lets a model propose a structured operation, such as looking up a record or calculating a result.

A document question-answering prototype needs a clear evidence path: find a relevant passage, provide it to a model and show where the answer came from.

An evaluation set is a collection of cases that show whether your AI application behaves as intended.

A rate limit is a signal to slow down or change how work is scheduled.

The Model Context Protocol provides a standard way for AI applications to connect with external tools and information.

Prompt injection occurs when content supplied to an AI system tries to redirect its behaviour.

Human review is effective when the reviewer sees the proposed action, supporting evidence and likely consequence.

API cost is a workflow problem, not just a model price.

A focused AI feature is easier to evaluate than an ambitious system with many tools and unclear success criteria.