Prompt Engineering, RAG or Fine-Tuning? Decision Playbook

About this course

This course contains the use of artificial intelligence.

Most enterprise LLM projects stall on the same question: do we prompt, retrieve, or fine-tune? Pick wrong and you spend six months and a large budget solving a problem the cheapest lever would have solved in three weeks.

This is not a twenty-hour AI-engineering bootcamp. It is a decision playbook. The deliverable is judgment — the ability to look at a use case and defend a customization decision in front of an architecture review board, a CISO, and a CFO.What makes this course different

Decision-first, not tool-first — every section ends by routing a real use case, not by finishing a tutorial

Enterprise constraints are first-class — cost, latency, privacy, data residency, model risk, and monitoring get real coverage, not an afterthought

One model company runs the whole course — you follow a single specialty insurer through three real use cases and watch one architecture evolve, rather than nine disconnected demos

Every section ships a reusable artefact — a decision matrix, an ADR template, a dataset-readiness checklist, a RAG evaluation workbook, a cost calculator, a threat-model template, and a production-readiness checklist

What you will actually doRoute use cases through a documented seven-question decision tree

Write and version a structured prompt with a frozen evaluation set

Specify a production retrieval pipeline and measure its retrieval half separately from its generation half

Run a lightweight LoRA fine-tune in Colab and audit a dataset for readiness

Model cost per thousand requests and find the volume where the ordering flips

Threat-model an LLM feature and complete a production-readiness review

Write an Architecture Decision Recor

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