Education · Prompt Engineering
Prompt Engineering
Five modules that take you from “the AI ignored my instructions again” to writing clear, structured, cost-aware prompts that get usable output the first time — every time. The full video walkthrough is on YouTube too.
Sample prompt
v3 · 38 tokens## Context
You are an SEC filings analyst.
## Task
Extract every risk factor from the section below.
## Input
{filing_text}
## Output
JSON { risks: [{ title, severity }] }
Why this is leverage
Three reasons prompt engineering moves the needle
Most AI projects fail at the prompt before they ever get to the model. Three concrete reasons it is worth taking seriously.
Better outcomes
A precise prompt gets a precise answer. Quality goes up, hallucinations go down, and reviews stop being rewrites.
Lower cost per call
Tight prompts use fewer tokens. Over thousands of calls per day, that turns into a real line item back in your budget.
Faster iteration
Clear instructions and good examples cut the trial-and-error loop from days to hours.
The five modules
From “why” all the way to “refine at scale”
Each module has one clear job, ends with hands-on practice, and feeds the next.
Module 01
Introduction to prompt engineering
Why prompts are leverage — and where the biggest wins come from on day one.
- What a prompt actually is
- Why it matters more than model choice
- The cost of a bad prompt
Module 02
Anatomy of a prompt
The five-part skeleton every reliable prompt has — context, instruction, input, examples, output format.
- Context vs instruction
- Examples and shot count
- Specifying output schema
Module 03
Core principles
The non-negotiables: clarity, specificity, structure, and the discipline of showing instead of telling.
- Clarity over cleverness
- Specificity tightens output
- Structure beats prose
Module 04
Advanced techniques
Once the basics work, this is where you compound. Zero-shot, few-shot, chain-of-thought, prompt chaining.
- Few-shot patterns
- Chain-of-thought when it helps
- Prompt chains that compose
Module 05
Iteration strategies
How to compress without losing accuracy, refine without regressing, and verify outputs at scale.
- Compression without loss
- Refinement loops
- Verification at scale
A peek inside
The anatomy of a prompt you can trust
Every reliable prompt is built on the same five parts. The module on anatomy walks you through each in detail, with examples and counter-examples from real production prompts.
Who the AI is and the world it operates in.
The specific task — phrased as an action.
The actual content the AI needs to operate on.
One or more known-good input → output pairs.
JSON, markdown, schema — whatever downstream consumers expect.
Who this guide is for
Three reader profiles get distinct value. None of them are “people who want to learn AI in general.”
AI developers & engineers
Cut your debugging loops. Ship features that work first time instead of after seven prompt tweaks.
Product managers
Write requirements precise enough that engineering can implement them — and AI can execute them — without back-and-forth.
Researchers & analysts
Get structured, reproducible outputs from LLMs instead of long prose you have to manually parse every time.
Ready to write prompts your future self will not have to rewrite?
Watch the full YouTube series, run the labs, and start shipping AI features that hold up under real usage.