Mastering the
Prompt.

Pick your track

"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.

The full course map

Prompting FAQ

Short, direct answers to the questions people ask most about prompting and AI models.

What is prompt engineering?

Prompt engineering is the practice of writing clear, specific instructions that get a large language model to do what you want. A strong prompt names the task, gives the model the context it needs, and specifies the output format. It is iterative: you test, see where the model misreads, and refine.

How do you write a good prompt?

Use five components: a role, a clear task, the relevant context, the output format, and any constraints. State what you want the model to do rather than what to avoid, give the reason behind important rules, and put your key instruction at the start or end of the prompt, where the model attends most.

What are the components of a prompt?

Role (who the model should act as), Task (the single objective, always required), Context (audience, background, source material), Format (the shape of the output), and Constraints (limits, tone, length). Start with the Task and add the others only when the result misses.

What is the difference between zero-shot and few-shot prompting?

Zero-shot prompting gives only instructions and no examples; few-shot prompting includes a handful of input-output examples to lock in a format or behaviour. Try zero-shot first, especially on reasoning models, and add 2 to 5 clean, varied examples only when you need to pin down a specific style or edge case.

How do you prompt reasoning models differently?

Reasoning models think before answering, so older scaffolding can hurt them. Give a clear goal, hard constraints, and an output contract, then let the model reason. Skip "think step by step" and heavy few-shot examples; if reasoning is the gap, raise the effort or thinking setting instead of rewriting the prompt.

How do I stop an AI from making things up (hallucinating)?

Ground the model in sources, ask it to cite the evidence for each claim, and tell it to say "I don't know" when the information is not there. Add a verification step with named criteria, and for anything high-stakes, check the facts rather than trusting fluent prose.

What is chain-of-thought prompting?

Chain-of-thought prompting asks the model to reason step by step before giving a final answer, which historically improved math and logic tasks. On modern reasoning models it is often unnecessary or even counterproductive, since they already reason internally, so test whether a simpler prompt does better first.

Which AI model should I use for which task?

Use a fast, inexpensive model for simple, well-defined work like classification, extraction, and formatting, and reserve the large reasoning model for ambiguous, multi-step, or high-stakes problems like analysis, planning, and complex code. Running the heaviest model on light tasks is the fastest way to burn through your rate limits and budget.

What is a system prompt?

A system prompt is the top-level instruction that defines the model's role, the rules it must follow, and the deliverable for a whole task or application. Keep it lean: front-loading every possible rule forces the model to carry all of it even on trivial requests.

What is RAG (retrieval-augmented generation)?

RAG grounds a model's answer in documents you retrieve and place into the prompt, so it can use current or private information beyond its training data. Wrap each source in tags with its origin, ask the model to extract the relevant quotes first and answer only from those, and require inline citations.

What is a context window?

The context window is the total amount of text, measured in tokens, that a model can consider at once, including your prompt, any documents, the conversation history, and its own answer. Models attend most to the start and end of the window, so place critical instructions there rather than burying them in the middle.

How do I get better results from ChatGPT, Claude, or Gemini?

Be specific, give context, and say what you want explicitly. Ask the model to interview you for any missing context before it answers, request the exact output shape, and iterate rather than expecting a perfect first try. Treat it like a brilliant new colleague who has zero knowledge of your goals.