The Complete Prompt Engineering Course
A standalone, comprehensive guide to getting the best possible results from large language models — from how a model actually reads your words, through the core prompting toolkit, reasoning techniques, structured output, retrieval and context, tools and agents, all the way to evaluating and shipping prompts in production. Every technique is shown with real prompts and the responses they produce, so you learn by seeing what works and why.
Phase 1 · Foundations
What prompting is, how language models read your words, and the parts every prompt is built from.
Introduction to Prompt Engineering
What prompt engineering really is, why the same model can feel brilliant or useless depending on how you ask, and a map of everything you'll master in this course.
How LLMs Actually Work
Just enough of the machine to prompt it well: a language model predicts the next token, one at a time, from everything it has seen so far — which explains almost every quirk you'll meet.
Tokens, Context Windows & Cost
Models don't see words — they see tokens. Understand tokenization, the context window that holds your whole conversation, and how both drive latency, limits, and cost.
The Anatomy of a Prompt
Every strong prompt is built from the same handful of parts: role, instruction, context, input data, examples, and an output specification. Learn each one and how they fit together.
Phase 2 · The Prompting Toolkit
The everyday techniques: clear instructions, roles, examples, structure, and shaping the output.
Writing Clear Instructions
The single highest-leverage skill: say exactly what you want. Specificity, positive framing, ordering steps, and removing the ambiguity that sends a model off track.
Roles, Personas & System Prompts
Tell the model who to be. How assigning a role and writing a strong system prompt sets tone, expertise, and behavior for an entire conversation.
Zero-shot & Few-shot Prompting
Sometimes the fastest way to explain a task is to show it. Zero-shot vs. few-shot, how to choose and format examples, and the traps that make examples backfire.
Structure, Delimiters & Formatting Input
A well-organized prompt is a well-understood prompt. Use delimiters, headings, and markup to separate instructions from data and keep long prompts unambiguous.
Controlling the Output
Shape exactly what comes back: length, format, tone, and style. Specify the output the way you'd brief a colleague, and stop fighting walls of unwanted text.
Phase 3 · Making Models Reason
Get a model to think before it answers — chain-of-thought, decomposition, and advanced reasoning.
Chain-of-Thought Prompting
The technique that unlocked reasoning: ask the model to think step by step before answering, and watch accuracy on hard problems jump.
Task Decomposition & Prompt Chaining
Big tasks fail as one giant prompt. Break them into a chain of small, reliable steps where each prompt's output feeds the next.
Self-Consistency & Ensembling
Ask more than once. Sample several independent answers and take the consensus to squeeze more reliability out of the same model.
Advanced Reasoning: ReAct, Reflection & Tree-of-Thought
Beyond a single chain of thought: interleave reasoning with actions (ReAct), let a model critique and revise itself, and explore multiple reasoning paths.
Phase 4 · Structured & Reliable Output
Make output machine-readable and repeatable: JSON and schemas, constraints, and the sampling dials.
Structured Output: JSON & Schemas
To wire a model into software, its output must be machine-readable. Get clean JSON every time with schemas, examples, and the model's structured-output modes.
Constraints, Rules & Guardrails
Keep the model inside the lines. Hard rules, allow/deny lists, and guardrails that stop a model from wandering off-task or off-brand.
Temperature, Top-p & Sampling
The dials behind creativity and consistency. See how temperature, top-p, and top-k reshape the model's next-token choices — and when to turn them up or down.
Consistency & Reliability at Scale
A prompt that works once isn't done. Make prompts robust across thousands of varied inputs — handling edge cases, empty inputs, and quiet failures.
Phase 5 · Grounding & Knowledge
Beat hallucination by feeding the model the right facts — RAG, long context, and context engineering.
Why Models Hallucinate — and How to Ground Them
Models make things up with total confidence. Understand why hallucination happens and the prompting moves that keep answers tied to real, provided facts.
Retrieval-Augmented Generation (RAG)
Give the model an open book. RAG retrieves relevant documents and drops them into the prompt so answers are grounded in your own, up-to-date data.
Working with Long Context & Documents
Modern models can read whole books at once — but bigger isn't automatically better. Learn placement, the 'lost in the middle' problem, and summarization strategies.
Context Engineering
Prompting grows up: deliberately managing everything in the model's window — instructions, memory, retrieved facts, and tools — as a limited, valuable resource.
Phase 6 · Tools, Agents & the API
Go beyond chat: call the API, give the model tools, and build agents that take actions.
Prompting Through the API
Leave the chat box behind. Send prompts programmatically: the messages format, system prompts, key parameters, and reading a response in Python.
Tools & Function Calling
Let the model do things: search, run code, hit an API. Define tools, describe them well, and handle the model's calls — the foundation of every agent.
Building Agents: The Reasoning Loop
An agent is a model in a loop with tools and a goal. See the think-act-observe cycle, how to prompt for it, and how to keep an autonomous run on the rails.
Multi-Agent Systems & Workflows
One agent isn't always enough. Coordinate specialized agents — planners, workers, and critics — and know when a simple workflow beats a crowd.
Phase 7 · Production Prompting
Ship prompts you can trust — evaluation, debugging, security, and responsible use.
Evaluating & Testing Prompts
Stop guessing whether a prompt is better. Build a test set, define metrics, and use LLM-as-judge to measure prompt quality objectively.
Iterating & Debugging Prompts
A repeatable loop for turning a mediocre prompt into a great one: read failures closely, change one thing at a time, and let the model help debug itself.
Prompt Injection & Security
When your prompt meets untrusted text, attackers can hijack it. Understand direct and indirect prompt injection, data exfiltration, and the defenses that actually help.
Safety, Bias & Responsible Prompting
Powerful tools carry real responsibility. Bias in outputs, privacy of what you send, transparency with users, and prompting in a way you can stand behind.
Phase 8 · Applied Prompt Engineering
Reusable patterns, domain playbooks, and where prompting goes next.
Prompt Patterns & Recipes
A working cookbook of reusable prompt patterns — from summarization and extraction to classification and rewriting — you can adapt to almost any task.
Domain Playbooks: Writing, Code, Data & Support
Prompting looks different in every domain. Concrete playbooks for content writing, coding assistants, data extraction and analysis, and customer support.
Multimodal & The Road Ahead
Prompting is expanding beyond text to images, audio, and video, and models keep getting stronger. Where the field is heading — and how to keep your skills sharp.