Prompting Mastery
A 55-minute deep dive for power users and developers — structured outputs, chain-of-thought, few-shot, evaluation, and the prompt-library habits that compound.
8
Chapters
~55 min
Duration
Intermediate
Level
No
Certification
Who this is for
For analysts, developers, and senior individual contributors who already use ChatGPT, Claude, or Copilot every day and want to move from ad-hoc prompts to repeatable, evaluable patterns.
How this course works
- 8 audio-narrated slide chapters · ~55 min of focused content
- Capstone with interactive Markdown builder you take to your team
- Trust trip-wires on every play — what not to cross
- Free verifiable certificate on completion
What you'll walk out with
Specific outcomes from this course — no fluff.
- Structured outputs that survive downstream parsing — JSON, XML, tables, citations
- Chain-of-thought prompting — when it helps, when it harms, how to constrain it
- Few-shot patterns that lift accuracy without bloating tokens
- How to build and curate a prompt library — the habits that compound over months
- Evaluation framework — how to know your prompt is good without running 1000 trials manually
- Cost + latency awareness — when to use which model, the 3-axis tradeoff (quality, cost, speed)
- Power-user tactics — temperature, system prompts, response shaping, error recovery
Course content
8 chapters · ~55 min
Prompting principles
The five things that separate amateur prompts from professional ones — and the chat-vs-reasoning model toggle that changes which principles dominate.
Structured outputs (JSON)
Stop parsing free text. Build a schema, force JSON Schema strict mode, and watch the model return parseable JSON on the first try every time.
Chain-of-thought reasoning
A 2×2 of when chain-of-thought earns its latency cost — and when adding "think step by step" makes outputs worse, not better.
Few-shot patterns
Examples that generalise vs. examples that memorise — a similarity scorer that shows the precise distance between your few-shots and your actual input distribution.
Evaluating outputs
A mini eval runner you can drop into CI tomorrow — happy path, edge cases, adversarial inputs — that turns "looks good to me" into a measured pass rate.
Prompt libraries and reuse
Four files per prompt — prompt, schema, evals, changelog — plus owner, versioning, and CODEOWNERS. The structure that survives turnover.
Edge cases and adversarial prompts
EchoLeak (CVE-2025-32711), CamoLeak, and Slack AI — the three 2025 incidents that prove prompt injection is now a production problem. The four defences that actually work.
Making your library stick
Owner, weekly review, evals in CI, frictionless contribution — the four things that decide whether the library lives past month three. The 4-week rollout plan, downloadable per team size.
Want this delivered inside your organisation?
The course is the starting point. The same content powers a 4-week pilot, an org-wide rollout, or a continuous build engagement — set up on your data, with your team, by Gennoor Tech.