Prompt Engineering for Practitioners
A 60-minute hands-on course for developers and senior analysts — structured outputs, tool use, evaluation, and prompt-as-code.
Last updated: 2026-05-19
What you'll learn
By the end of this course you'll be able to:
- Prompting principles that survive model upgrades and provider switches
- Structured outputs with JSON schemas and schema-enforced decoding
- Function calling and tool use across OpenAI, Anthropic, and open models
- Chain-of-thought, ReAct, and reasoning-elicitation patterns that actually help
- Few-shot vs zero-shot tradeoffs — and when each is the wrong choice
- Offline and online prompt evaluation, plus prompt-as-code workflows
Who this is for
Developers, ML engineers, senior data analysts, and applied scientists building LLM-powered features. Especially useful for engineers shipping prompts into production who need them to be testable, versionable, and reliable across model upgrades — not just clever in a notebook.
Curriculum
8 chapters · 2 hands-on exercises · capstone challenge
Each chapter ends with the learning objectives ticked off. Quizzes are auto-graded with feedback; exercises are open-ended and produce artifacts you can take to your team.
1. Prompting principles
- Apply the six prompting principles that generalize across model families
- Diagnose why a prompt works on one model and fails on another
2. Structured outputs
- Specify JSON schemas the model is forced to honor (OpenAI Structured Outputs, Anthropic tool schemas)
- Recover gracefully when constrained decoding still drifts
3. Function calling and tool use
- Design tool definitions that the model picks correctly under ambiguity
- Handle multi-tool orchestration, retries, and tool-call failures
4. Chain-of-thought patterns
- Use explicit reasoning, ReAct, and scratchpad patterns where they help
- Recognize when chain-of-thought hurts accuracy or inflates cost
5. Few-shot vs zero-shot
- Choose between zero-shot, few-shot, and dynamic example retrieval
- Avoid the few-shot leakage and overfit patterns
6. Prompt evaluation — offline and online
- Build a regression-style eval suite with golden cases and LLM-as-judge
- Wire online evals (sampled, human-in-loop) into your production traffic
7. Prompt versioning and prompt-as-code
- Version prompts alongside application code with diffable templates
- Roll prompts safely with canary releases and rollback hooks
Capstone: Capstone: Ship a production-grade prompt pipeline
- Design a versioned, evaluated, schema-enforced prompt for a real use case
- Document the eval, rollback, and observability plan that goes with it
Capstone deliverable: Every learner who completes this course produces «Your Production-Grade Prompt Pipeline» — a tangible artifact you take back to your organization.
Curriculum live · full chapter content rolling out through 2026.
The outline, learning objectives, references, and capstone deliverable are published. Full chapter content (video, narration, exercises) ships progressively. Get notified when each chapter goes live.
References & sources
Built on cited sources — not vibes.
Every course is researched fresh against vendor documentation, regulatory sources, and peer-reviewed work. Sources used in this course:
OpenAI Prompt Engineering Guide
OpenAI · Source link
Anthropic Prompt Engineering Documentation
Anthropic · Source link
Azure OpenAI Service — Prompt Engineering Techniques
Microsoft Learn · Source link
OWASP Top 10 for LLM Applications
OWASP Foundation · Source link
Course details
Track
Builder
Level
Intermediate
Audience
Technical practitioner
Function
IT & Engineering
Industry
Cross-Industry
Stack
Stack-agnostic
Paired Gennoor Way phase
innovate, build
Format
video, hands-on, interactive
You finished the course. Now what?
From course to outcome.
Reading this course is step one. The next step is applying it where you work. Here's how Gennoor helps — without the deck, without the pitch.
Run this for your team
A 2-day workshop or virtual cohort for up to 25 of your people, with exercises run on your data and a 30-day adoption plan.
From $5k · 2 weeks · function-specific
Apply this to your data
A 4–6 week pilot that takes what you learned and ships a working system inside your environment. Fixed scope, fixed price, code transferred day one.
From $25k · 6 weeks · production-grade
Just want to talk?
Free 30-minute call. No deck, no pitch. We listen to your situation and tell you honestly what makes sense — even if it isn't us.
Free · no commitment · 30 minutes
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