[100% Off] Prompt Engineering, Rag Or Fine-Tuning? Decision Playbook
Decide between prompting, retrieval, and fine-tuning — then evaluate cost, quality, security, and readiness.
Requirements
- Working familiarity with what an LLM is and how an API call works
- Comfort reading an architecture diagram
- No machine learning, maths, or fine-tuning experience required
- Optional, for the Colab lab only: basic Python literacy, as the notebook runs end to end without editing
Description
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 do
Route 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 Record recommending one approach, with rejected alternatives and their rationale
You need to know what an LLM is and how an API call works. You do not need a maths background, an ML background, or any fine-tuning experience.
Author(s): Dr. Amar Massoud
