A free, interactive masterclass · 2026 edition
A course on directing large language models, from your first prompt to agentic, reasoning-model, evaluation-driven mastery. Built to be done, not just read: every module carries labs, a rubric, and prompts you can run as you go.
Most people treat prompting as guessing the secret words. It isn't. A prompt is an engineered brief you hand to a capable but context-free collaborator. This is a course, not a cheat sheet: each module states what you'll be able to do, gives you something to practice, and shows you how to judge your own work.
Treat the model like a brilliant new colleague on their first day: phenomenally knowledgeable, eager, fast, but with zero knowledge of your goals, your norms, or what "good" looks like to you. Every technique here is a better way to brief that colleague. The golden test: if you showed your prompt to a real person with no extra context and they'd be confused, the model will be too.
The skill is identical for everyone; this only swaps the worked examples toward your world. Change it anytime, we'll remember your choice. See the full business prompt collection →
This page is built to WCAG-minded standards: semantic headings, a skip link, visible focus styles, text alternatives on diagrams, AA-contrast text, and full support for prefers-reduced-motion. Examples deliberately span many roles, domains, and one multilingual and one fairness-sensitive case. If you adapt this into slides or video, add captions, transcripts, and spoken descriptions of every diagram from the start rather than retrofitting later.
This course is designed outcomes-first: every lab and rubric maps back to these. By the end, you will be able to:
This revision teaches the modern reasoning-model default first (the reasoning-model module), then the historical techniques as context (the advanced-reasoning module). That ordering prevents a common trap: learning 2023-era scaffolding as if it were still the default, then having to unlearn it. You'll test claims against real models rather than memorising them.
"beginner to advanced" only works if you know which path is yours. Everyone does the Core modules; the rest depends on your track. No coding is required for two of the three. Pick one to tailor the course map below.
For: anyone using ChatGPT, Claude, or Gemini in a chat box.
Prereqs: none.
Do: Modules 1–4, 6, 8. Skim 5, 7, 9.
For: writers, analysts, marketers, ops, educators building repeatable workflows.
Prereqs: comfort with structured documents; JSON helpful, not required.
Do: all modules; labs in 3, 5, 7.
For: engineers wiring prompts into apps, agents, and pipelines.
Prereqs: APIs, JSON Schema, basic tool-use concepts.
Do: all modules + every lab + capstone.
Before any module, hold these four shifts in mind. The discipline split in two: models now reason on their own, and several classic "tricks" are now redundant, or counterproductive, on frontier models.
Flagship models (Claude Opus 4.x, OpenAI o-series / GPT-5, Gemini 2.5/3 with thinking) can "think" before answering. On those models, hand-written scaffolds like "think step by step" and elaborate few-shot examples are often unnecessary. The skill shifted from telling the model how to think to telling it what to achieve, but always test on the model you deploy.
The real job is curating the useful set of tokens in the window, system prompt, history, tool results, retrieved knowledge, not wording one perfect sentence.
Constrained decoding / structured outputs let you request schema-valid JSON with strong guarantees instead of pleading "please return valid JSON." This replaced a genre of fragile prompt hacks.
Instead of writing reasoning steps by hand, you can turn a dial: an effort / thinking control. The advanced move in 2026 is frequently to remove instructions and let the model's own reasoning run at the right intensity.
Advanced does not always mean more elaborate. On frontier reasoning models, the expert move is often to strip a prompt down, fewer examples, no "you MUST", no manual chain-of-thought, and instead give a clean goal, hard constraints, an output contract, and the right effort setting. Verify the effect on your target model rather than assuming it.
From the makers · put it to work
This manual is free because it is published by people who do this for a living. The course taught you to direct AI. These two put it to work, one for your business, one for your plan.
Task: [single objective] Constraints: - [scope, tone, exclusions] - [success criteria] Output: [exact sections / schema] Before finishing, check the result against [named criteria].
<documents> [paste your source text here] </documents> Question: [your question] 1. Extract the quotes relevant to the question. 2. Answer using only those quotes. 3. Cite each source inline. 4. If the evidence is thin or missing, say so plainly.
Grade the prompt below from 1-4 on: objective clarity, context sufficiency, output contract, robustness to ambiguity, and safety + evidence.
Return JSON: {"scores": {...}, "highest_risk": "...", "one_best_improvement": "..."}
Prompt to grade:
"""
[paste the prompt you want graded]
"""Acronyms are defined on first use in the modules; this is the quick reference.
| Zero-shot | Prompting with instructions only, no examples. |
| Few-shot | Providing example input→output pairs to steer behavior. |
| CoT (Chain-of-Thought) | Eliciting step-by-step reasoning before the final answer. |
| Self-consistency | Sampling multiple reasoning paths; taking the majority answer. |
| ReAct | Reason→Act→Observe loop; the basis of tool-using agents. |
| ToT (Tree-of-Thoughts) | Branching, scoring and pruning multiple reasoning paths. |
| Context engineering | Curating the useful set of tokens across a task. |
| Structured outputs | Constrained decoding that yields schema-valid output. |
| Effort / thinking budget | A control for how much a model reasons before answering. |
| System / developer message | The authoritative instruction defining role and rules. |
| RAG (Retrieval-Augmented Generation) | Grounding answers in fetched documents. |
| Prompt injection | Untrusted content trying to hijack the model's instructions. |
| LLM-as-judge | Using a model to score outputs against criteria at scale. |
Short, direct answers to the questions people ask most about prompting and AI models.
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