Writers, analysts, researchers, and PMs. A prompt is an instruction set. Engineers who write precise, testable, structured prompts get dramatically better results than those who treat AI as a magic box.
"Prompt engineering is the practice of giving clear instructions, useful context, examples, constraints, and output formats so an AI model can produce more reliable results."
— virat-lab / prompts
Everything else in this path builds on this one structure. Learn it, use it for a week, then move to the advanced patterns.
Role: You are a senior UX researcher. Goal: Help me write 5 user interview questions about [product feature or workflow]. Context: The interviewees are [user type]. We are trying to understand [specific thing you want to learn]. Avoid leading questions. Do not ask about hypothetical future behavior. Format: Numbered list. Each question followed by a one-sentence note on what insight it is designed to surface. Max 20 words per question.
The Role limits the AI to relevant domain knowledge. The Goal is unambiguous. The Context prevents common failure modes (leading questions). The Format makes the output immediately usable.
The curriculum covers 7 categories. Each has a pattern, a failure mode to watch for, and a copy-paste example you can use immediately.
Role-Goal-Context-Format. Zero-shot prompting. Defining clear, unambiguous objectives. Getting consistent outputs on repeatable tasks.
Structured summarization, comparative analysis, literature review, identifying gaps. Designed for deep, multi-document research tasks.
Prompts that produce structured, machine-readable outputs for downstream processing. JSON, CSV, tagged lists — for building AI into automated pipelines.
Prompts designed to resist injection, limit scope, and return safe outputs. Essential for production deployments and systems that process untrusted input.
Privacy-safe prompts for clinical contexts: research summaries, patient education, administrative support. Always designed around PHI protection and human review requirements.
Code generation, debugging, documentation, code review. Prompts that constrain language, version, style guide, and output format for reliable, usable code outputs.
Prompts for designing processes, mapping workflows, analyzing systems, and creating documentation. For operations, engineering, and business professionals.
Use when the task requires multi-step reasoning, math, logical analysis, or deduction. The model externalizes its reasoning, which also makes errors easier to spot.
Provide 2-3 input/output examples before the actual request. Use when the task has a specific style, tone, or structure that is hard to describe but easy to demonstrate.
Define the exact output schema in the prompt. For JSON: include field names and types. For tables: name every column. For lists: specify count, order, and what each item must contain.
These prompts help you build and improve other prompts. The most valuable skill in prompt engineering.
Role: You are a prompt engineer who specializes in making prompts more reliable. Goal: Improve this prompt so it produces more consistent, high-quality outputs. Context: I use this prompt for [task]. Current problems I experience: [describe issues: inconsistent format, wrong tone, too verbose, hallucinations, etc.]. Format: (1) Diagnose what is wrong with the current prompt in 3 bullets, (2) Provide an improved version, (3) List 2 variations to A/B test against it. Current prompt: [paste your existing prompt here]
Role: You are a prompt engineer specializing in structured outputs. Goal: Design a prompt that reliably produces [desired output format: JSON / table / structured list / report]. Context: I need this output for [use case]. The output will be [processed by code / read by humans / inserted into a template]. Failure mode I want to avoid: [describe what goes wrong now]. Format: Give me a complete prompt template with explicit output format constraints. Include a validation checklist I can use to check if an output is compliant.
Role: You are a prompt engineer. Goal: Help me create 3 high-quality few-shot examples for this task: [describe the task]. Context: The model currently [describe the failure mode: wrong tone / wrong format / misses the point]. I want outputs that [describe ideal characteristics]. Format: For each example: INPUT: [what the user provides] / OUTPUT: [the ideal response]. After the 3 examples, explain in 2 sentences what makes them effective as training examples.
Designs, tests, and maintains structured prompts for production AI systems. Builds reusable libraries, runs A/B evaluations, and ensures reliable output quality at scale.
Prompt Engineer · AI Content Strategist · AI Product Manager · LLM QA Specialist · AI-Enabled Analyst
All 8 career tracks →Prompt Engineering is the foundation skill. These paths are where you apply it — to real automations, real agents, and real domain-specific problems.
Take your prompts and wire them into actual automated workflows. The prompts you've learned here become the core of every automation you build.
Start →System prompts, tool descriptions, and agent instructions are all prompt engineering. If you want to build agents, these skills are the prerequisite.
Start →Apply prompt engineering to security — threat modeling, incident analysis, policy writing, and red teaming. Your prompt skills go further with a security lens.
Start →