One search. Connected ideas.

A planner proposes steps; an executor performs permitted steps and reports results. Splitting the roles separates deciding from acting. The executor still enforces constraints rather than blindly following the plan.

Practice saying the answer clearly, then explain the insurance example and the trade-off. A strong answer identifies the authoritative data, safe failure, and measurement—not only the framework name.

Beginner — What makes a good prompt?

Beginner — What is RAG?

Beginner — Explain an embedding.

Beginner — Why use a vector index?

Beginner — What does LangChain provide?

Beginner — What are nodes, edges, and state?

Beginner — Agent versus chatbot?

Beginner — What problem does MCP solve?

Beginner — What is MAF?

Beginner — Deterministic versus agentic workflow?

A planner proposes steps; an executor performs permitted steps and reports results. Splitting the roles separates deciding from acting. The executor still enforces constraints rather than blindly following the plan.

Practice saying the answer clearly, then explain the insurance example and the trade-off. A strong answer identifies the authoritative data, safe failure, and measurement—not only the framework name.

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.

Beginner — What makes a good prompt?

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.

Beginner — What is RAG?

If the task already identifies a small permitted document set, long context may be simpler. For a large changing corpus, retrieval selects relevant evidence more efficiently. Both need access control, applicability checks, and answer evaluation.

Beginner — Explain an embedding.

It compares vector direction and helps rank related content. A high score is not a probability that the passage is true or sufficient. I use a compatible metric and calibrate thresholds on domain-specific queries.

Beginner — Why use a vector index?

Exact search examines eligible vectors thoroughly; approximate search trades some nearest-neighbor recall for speed and scale. I benchmark with realistic filters, corpus sizes, and query patterns rather than assuming ANN is always required.

Beginner — What does LangChain provide?

AI Engineering learning module

AI Engineering learning module