Prompt Engineering & Tooling

AI Learning Hub

Context is everything. Structure gets rewarded. You are the retrieval system.

4 tracks 5 skills 90 min live + self-paced maintained 2026

Four steps, then you're off

Whether you're brand new or have been prompting for a year, this path covers the foundation before branching into role-specific work.

01

Why AI?

What it actually does well, what changes for your role, and why prompting is closer to programming than talking.

read it here
02

Pick Your Tool

Chat assistant, code completion, AI-native editor, or agentic IDE — don't overthink it, but know the difference.

tool landscape
03

Learn the Patterns

Zero-shot, few-shot, chain-of-thought, ReAct, Tree of Thoughts — foundations and advanced patterns, with a second brain framework.

prompting patterns
04

Pick Your Role Track

Dev, PO/PM, Delivery Lead, Tech Lead, or capstone. Role-specific workflows built on the same underlying patterns.

choose a track

Before you learn the patterns, orient yourself

Three things worth internalizing before you touch a prompt library or install a tool.

What AI actually does well

The tasks where it earns its keep

  • Drafting first passes on anything text-shaped (emails, docs, PRDs, summaries)
  • Transforming content — reformatting, translating register, restructuring arguments
  • Recognizing patterns in messy inputs (logs, feedback, transcripts)
  • Writing boilerplate and scaffolding you'd otherwise copy-paste
  • Rubber-ducking your own thinking — externalizing and stress-testing ideas
What changes for your role

The same tool, different leverage points

Developer Specs and test cases arrive faster than code. The bottleneck shifts to review and integration, not generation.
PO / PM User stories with Given/When/Then, sprint backlogs, and roadmap rationales in seconds instead of hours.
Delivery Lead Risk matrices, status reports, and onboarding plans generated from your notes — you edit, not author.
Tech Lead Run parallel architecture evaluations, generate ADRs via Tree of Thoughts, and build metaprompts that amplify your whole team.
The mental model shift

Prompting is programming

A prompt isn't a request. It's a program. The same engineering principles that make code maintainable make prompts effective.

In code
Database
In a prompt
Context
Why it matters
What you include determines what the model can retrieve
In code
Schema
In a prompt
Structure
Why it matters
Format and constraints shape the output space
In code
Query / retrieval
In a prompt
You
Why it matters
You decide what enters the context — that is the skill

Four categories of AI tooling

Most people start with whatever's available and upgrade when they hit a ceiling. That's the right move. Here's what the categories actually mean.

Chat Assistant Code Completion AI-Native Editor Agentic IDE
What it is Conversational interface for open-ended prompting
e.g. ChatGPT, Claude.ai, Copilot Chat
Inline suggestions as you type, trained on code
e.g. GitHub Copilot, Tabnine, Codeium
Editor with deep AI integration — inline editing, codebase awareness
e.g. Cursor, Windsurf
Agent that reads, edits, and runs code autonomously across files
e.g. Claude Code, Devin, Copilot Workspace
Best for Drafting, explaining, brainstorming, non-code tasks Speeding up typing in familiar codebases; boilerplate Refactoring, multi-file edits, test generation, codebase Q&A Large refactors, new features with specs, automated review loops highest leverage
Primary users Everyone — all roles benefit Developers (and technically-minded POs/TLs) Developers who want more than autocomplete Senior devs and tech leads comfortable giving AI significant scope
Context window Single conversation; loses context across sessions Current file + a few nearby files Whole codebase via embeddings or selection significant upgrade Repo-wide; can run commands, read logs, write tests maximum scope
Learning curve Low — natural language interface start here Low-medium — mostly passive until you learn to steer it start here Medium — new prompting patterns, composer mode, rules files High — requires spec discipline and trust calibration graduate when ready
When to start Day one — use it for anything you'd Google Week one if you write code regularly When you're writing AI-assisted features or doing large refactors When you can write a spec that an agent can execute without babysitting

Materials for the live workshop

Self-study first, then the live sessions, then keep the reference cards close.

📋

Common Knowledge

15-minute self-study. Complete before the live session to align on foundational concepts and vocabulary.

Prereq materials
🎤

Session 1 — Patterns & Priority Builder

60 min. Three Approaches Framework, foundational patterns, and a hands-on priority builder exercise.

Session 1
🎯

Session 2 — Advanced Patterns & Interview Prep

60 min. ReAct, Tree of Thoughts, and a complete interview preparation workflow using spec-kit methodology.

Session 2

Quick Reference Cards

Pattern recognition guide and decision tree for rapid lookup during practice. Printable and screen-friendly.

Quick reference

When you want more than the workshop covers

Advanced Patterns

Self-consistency, constitutional AI, chain-of-density, structured generation, and evaluation loops.

prompting-advanced

Prompt Cheat Sheet

Composition patterns, operator reference, and the scaffolding primitives behind every skill file.

cheat sheet

Diagrams as Prompts

Using Mermaid diagrams as structured reasoning inputs. Why pictures beat paragraphs for complex specs.

mermaid prompts

Lattice-Driven Dev

Dependency-ordered development methodology. Build L1 before L2, verify before shipping each layer.

lattice dev

For facilitators

Session structure, timing, and materials for running the live workshop.

Session flow — 105 min total

15 min Prereq Self-study before the call
15 min Activation Framing & three intuitions
40 min Role Fork Role-specific deep dive with skill file
20 min Capstone Build your own skill
10 min Close Synthesis & next steps
📚

Facilitator Guide v2

Minute-by-minute script, timing notes, failure modes, and contingency plans for delivery.

Facilitator guide
👥

Participant Materials

Decision matrices, demo personas, spec-kit templates, and workshop completion checklist.

Participant materials

v2 improvements

  • Faster — 90 min instead of 120 (still covers more ground)
  • Role-specific — four parallel tracks instead of one generic path
  • Agency — participants build their own skill file in the capstone
  • Lower entry friction — 15-min prereq removes baseline alignment overhead from live time
  • Production-grounded — examples drawn from real project patterns, not textbook exercises
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