MODERN AI
Prompt Engineering
Learn how prompts guide language models — from clear instructions and examples to structured outputs, context management, evaluation, security and production AI workflows.
Prompt Engineering Learning Path
Start with the foundations of prompting, then build toward reliable prompts for RAG, tool calling, agents and production systems.
What Is Prompt Engineering?
A simple, beginner-friendly introduction to prompts and Prompt Engineering, using everyday examples like writing messages, planning meals, and getting advice — before building up to why wording and structure matter.
Prompt Anatomy
The simple building blocks of a good prompt — what to do, background info, examples, rules, and format — explained with everyday examples like planning a party and writing a text message, not technical jargon.
Zero-Shot Prompting
What zero-shot prompting means in plain language — just asking directly, with no examples — when it works great for everyday requests, and when it starts to fall apart, shown with simple, relatable examples.
One-Shot and Few-Shot Prompting
What to do when just describing what you want isn't enough — showing the AI one or more examples instead, with everyday, developer, and agentic examples, plus the real trade-offs of adding examples to a prompt.
Role / Persona Prompting
What actually happens (and what doesn't) when you tell an AI 'you are a ___' — the real purpose of role prompting, its genuine limits, and when it helps versus when it's just a placebo phrase.
Instruction Following
How to write clear, unambiguous instructions an AI can actually follow reliably — explicitness, ordering, priorities, positive vs. negative instructions, and what happens when instructions quietly conflict.
Delimiters and Context Separation
A simple habit for keeping your instructions and your actual content clearly separated — why it improves readability and reliability, and why delimiters alone are not a security boundary.
Output Format Control & Structured Outputs
How to reliably get JSON, tables, or other specific structures back from an AI, instead of leaving the shape of the response to chance — and why 'return JSON' and a precise schema are very different requests.
Constraints
How limits like word counts, allowed values, and scope boundaries improve reliability — and the real, easy-to-miss trade-off between too few constraints and too many.
Chain-of-Thought and Reasoning
Why multi-step problems trip up AI more than simple ones, what asking an AI to 'show its reasoning' actually does, and how to use reasoning-oriented prompting for really more reliable answers — not as a magic phrase.
Task Decomposition
Why breaking a complex task into smaller pieces often works better than one giant prompt, when decomposition is worth the added complexity, and how it connects directly to AI workflows and agents.
Self-Consistency
What it means to generate multiple reasoning paths and compare them for a more reliable answer, how this differs from simply asking once, and when the extra cost is really worth it.
Prompt Chaining
Connecting the output of one prompt directly into the input of the next — formalizing task decomposition into real, working pipelines — and the genuine trade-offs in reliability, response time, cost, and debugging.
Iterative Prompting
Treating prompt design as a repeated process of testing and refining, not something you get perfect on the first try — how professional prompt development actually works, closing out Level 3.
System, User, and Assistant Messages
How modern LLM APIs structure conversations into system, user, and assistant messages — and how this structure directly powers chat apps, memory, agents, and tool calling.
Context Management
What happens as a conversation or prompt grows too large, why more context isn't automatically better, and the real strategies — truncation, summarization, retrieval — for managing it deliberately.
Prompt Engineering for RAG
How prompting changes when an AI receives retrieved documents alongside a question — grounding, citations, handling missing information, and the real, common mistakes that make RAG systems unreliable.
Prompt Engineering for Tool Calling
How prompts influence whether and how an AI decides to use a tool — tool descriptions, required parameters, and when-to-use rules — the direct bridge into how AI agents actually operate.
Prompt Engineering for AI Agents
Bringing task decomposition, chaining, tool calling, and context management together — how an agent's prompt is not just a request, but its entire behavior and control system, including goals, state, memory, and stop conditions.
Prompt Evaluation
Why a prompt that works once is not necessarily a good prompt — test datasets, quality criteria, and treating prompt evaluation as a genuine engineering problem, not a matter of eyeballing a result.
Prompt Versioning and Templates
Why prompts should be treated like code — change tracking, testing before deploying, rollbacks — and how reusable prompt templates make prompts maintainable across a real application.
Hallucination and Prompting
Why prompting alone cannot eliminate hallucinations, what 'don't hallucinate' actually fails to do, and the real design choices — grounding, retrieval, verification — that really reduce unsupported claims.
Prompt Injection
From first principles: why an AI can be tricked by text that looks like instructions, the real difference between trusted instructions and untrusted input, direct vs. indirect injection, and why prompting alone cannot fully solve this.
Jailbreaking
An educational, security-focused look at what jailbreaks are, why they occur, how they differ from prompt injection, why safety systems exist, and why prompt engineering cannot guarantee bypass resistance — closing Level 5.
Prompt Length and Token Economics
Prompt tokens, output tokens, context windows, cost, and response time — and the really important trade-off: more instructions is not automatically better results, with real prompt optimization approaches.
Model-Specific Prompting & Generation Parameters
Why the same prompt doesn't behave identically across different models, and how generation parameters like temperature and top-p relate to — but are really distinct from — prompt design itself.
Prompt Optimization
Pulling together evaluation, versioning, token economics, and model/parameter choices into one systematic optimization process — a complete, repeatable workflow rather than scattered individual techniques.
Production Prompt Engineering
How prompts work as part of a real application, not isolated text — observability, logging, fallback strategies, human review, and treating a production prompt as a genuine piece of the system, closing Level 6.
Context Engineering
Why modern AI systems often need more than a carefully written prompt — instructions, retrieved information, memory, tool results, and state, assembled together — and how Context Engineering extends everything covered so far.
Design Patterns, Case Studies & Mini Projects
Reusable prompt engineering patterns named and organized for quick reference, plus five realistic case studies and mini projects showing how this entire course comes together in practice.
Comparisons and Misconceptions
Prompt Engineering vs. Fine-Tuning, RAG, Model Training, Context Engineering, and Agent Engineering — clear, honest boundaries between each — plus a direct correction of the most common misconceptions about this field.
Interview Masterclass & Practical Exercises
The final module of this course: a complete summary of everything covered, a full cheat sheet, a comprehensive interview question bank, and hands-on practical exercises tying the entire course together.
WHY PROMPT ENGINEERING?
Turn language-model capability into reliable behavior
Prompts shape how an AI system interprets a task, uses context, formats its response and works with tools. These modules connect everyday prompting techniques to reliable RAG, agent and production workflows.