Prompt Engineering Crash Course
Zero-shot won't cut it. Learn the advanced techniques that actually ship: few-shot extraction, chain-of-thought reasoning, ReAct agents, and prompt evaluation arrays, all with real Python code.
What You Will Learn
- ✓Write prompts that return structured, reliable outputs every time
- ✓Apply few-shot, chain-of-thought, and self-consistency techniques
- ✓Build reusable prompt templates for any LLM provider
- ✓Design a ReAct agent that reasons and takes action autonomously
- ✓Optimize prompts systematically instead of guessing
Who Is This For
- Software engineers who who want to move beyond basic ChatGPT usage and build reliable AI features
- Backend developers who integrating LLMs into production systems and tired of unpredictable outputs
- AI-curious engineers who who learn best by writing code, not just watching theory lectures
Curriculum
Prompt foundations
4 lessons
Core techniques
5 lessons
Advanced patterns
4 lessons
Agentic prompting
5 lessons
Frequently Asked Questions
- Do I need to know Python?
- Yes, basic Python knowledge is required. If you are new to Python, take the Python for GenAI Beginners course first.
- Which LLM provider do I need?
- We use LiteLLM so you can use any provider: OpenAI, Anthropic, Google AI Studio, or others. A free Google AI Studio key works fine.
- Is this just prompt tips I can find on Twitter?
- No. This course covers production patterns like structured outputs, chain-of-thought pipelines, and ReAct agents, with working Python code, not one-liners.
- How long does it take to complete?
- About 6 hours if you work through the exercises. You can go at your own pace.
Prompt Engineering Crash Course
Zero-shot won't cut it. Learn the advanced techniques that actually ship: few-shot extraction, chain-of-thought reasoning, ReAct agents, and prompt evaluation arrays, all with real Python code.
Chosen by engineers moving beyond ChatGPT
The "Vibes" Evaluation. Sound familiar?
The Try-and-pray method
You tweak a few adjectives in your prompt, run it exactly once, say "looks good to me", and push it to production.
Brittle system prompts
Your mega-prompt works for the happy path, but the moment a user asks a slightly edge-case question, the LLM hallucinates wildly.
Missing guardrails
You asked the LLM to write a SQL query, and it returned the query wrapped in an explanation that broke your parser.
The evaluation nightmare
When a new model drops, you have no way of knowing if it will break your app because you have no automated evaluation suite.
The fix isn't adding "think carefully".
It's architectural prompting.
Senior engineers aren't better because they know more syntax. They're better because they've:
Prompt engineering isn't about talking to a machine. It's about constraining its output.
Code over theory.
Real Python applications.
We don't do browser-based prompt playgrounds. We write Python code. You will build actual extraction pipelines, agent loops, and evaluation suites using LiteLLM.
The prompt maturity path
From zero-shot to autonomous agents.
Context & formatting
Master zero-shot structures, system personas, and forcing valid JSON outputs.
Example-driven
Implement few-shot and many-shot prompting to radically improve classification accuracy.
Reasoning pipelines
Build chain-of-thought and self-consistency loops for complex logic problems.
Autonomous action
Combine everything to build a ReAct agent that loops through thoughts, actions, and observations.
From Zero-shot to Production Prompts.
Prompt foundations
Learn the core building blocks of effective prompts: structure, clarity, roles, and data separation.
Basic prompting techniques
Learn the 3-part prompt structure (Context, Task, Format) and how small changes produce dramatically different results.
Role-based prompting
Use system prompts and personas to unlock specialized knowledge and consistent behavior from LLMs.
Output formatting and structure
Control LLM output with XML tags, prefilling, and JSON mode to get machine-readable responses.
Prompt templates and variables
Build reusable prompt templates with data separation to prevent injection and improve maintainability.
Core techniques
Master few-shot prompting, chain-of-thought reasoning, self-consistency, and prompt chaining.
Few-shot prompting
Teach LLMs by example using zero-shot, one-shot, and few-shot patterns to control style, format, and behavior.
Chain-of-thought basics
Learn why LLMs fail at reasoning and how the "think step by step" instruction fixes it.
Chain-of-thought in practice
Apply CoT with thinking tags, solve real problems, and learn when CoT helps vs. when to skip it.
Self-consistency and voting
Improve accuracy by generating multiple reasoning paths and using majority voting to find the best answer.
Prompt chaining pipelines
Build multi-step workflows where the output of one prompt feeds into the next for complex tasks.
Advanced patterns
Tackle iterative refinement, guardrails, and meta-prompting for production-grade prompt systems.
The Generate-reflect-refine loop
Learn the 3-step iterative refinement pattern and build a complete Generate-Reflect-Refine pipeline in Python.
Applying iterative refinement
Practice the refinement loop on real tasks and learn when to iterate vs. when one pass is enough.
Constraints and guardrails
Add rules, limits, and safety constraints to keep LLM output accurate and on-topic.
Meta-prompting
Use AI to write and optimize prompts, the ultimate prompt engineering technique.
Agentic prompting
Build agent-like systems with ReAct prompting, multi-turn conversations, and evaluation pipelines.
ReAct pattern fundamentals
Learn the Reason + Act pattern, define tools for LLMs, and build the core agent loop in Python.
React in practice
Apply the ReAct pattern to real problems, compare it to prompt chaining, and test multi-step tool use.
Multi-turn conversations
Design routing and orchestration patterns for complex multi-turn conversation flows.
Prompt evaluation pipelines
Build automated scoring systems to measure prompt quality with test cases and LLM-as-judge.
Prompt optimization and a/b testing
Use meta-prompting to generate prompt variants and automated A/B testing to find the best one.
Start learning for free
Create your free account and start building.
Amateurs write. Engineers orchestrate.
Hacks are easy.
Anyone can add "Take a deep breath" to the end of a prompt.
Architecture is hard:
- Dynamically select few-shot examples based on user intent
- Implement multi-turn stateful ReAct loops
- Design prompt templates that prevent prompt injection
- Build automated LLM-as-a-judge evaluation pipelines
Stop treating prompts like magic spells. Treat them like API configurations.
Learn From Someone Who Actually Does This
I am the Cofounder of AEOsome.com and Chief Mentor at learnwithparam.com, with 15+ years of software engineering experience building production systems.
"Most engineers are not blocked by ability, but by lack of real system ownership."
Guest Sessions From Engineers at
Live sessions on System Design, Career Growth, and Interview Preparation.
Ready to Start Learning?
This entire course is free. No credit card required. Enroll and start building.
Frequently Asked Questions
Do I need to know Python?
Which LLM provider do I need?
Is this just prompt tips I can find on Twitter?
How long does it take to complete?
6 hours from now:
Stop wishing. Start engineering.