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Courses
Learning tracks
Projects & challenges
- Build a Personal Study Assistant
- Build a Research Brief Generator
- Ship a Portfolio Site in an Afternoon
- Run an Honest CV Review
- Debug a Real Bug With AI
- Write Landing Page Copy That Says Something
- Clean a Messy Dataset With AI
- Turn a Spreadsheet Into a Dashboard
- Build a Meeting Notes System
- Set Up a CRM for a Small Business
- Plan a Month of Content Without Burning Out
- Build Your Own Prompt Library
- The Explanation Ladder Challenge
- The Fabrication Hunt
- The Constraint Stack
- The Same Message, Five Ways
- Review Code the Model Wrote
- Refactor Without Breaking It
- The Cover Letter That Is Not Generic
- The Hostile Interview
- Summarise Without Losing the Point
- The Wrong Chart Challenge
- The Requirements Interrogation
- Two Versions, One Difference
Free tools
Prompt categories
Articles
- What Is Prompt Engineering? A Practical Guide for 2026
- 10 Prompt Mistakes That Make AI Output Generic
- GPT vs Claude vs Gemini: An Honest Comparison
- Which AI API Is Cheapest? How to Actually Compare
- How Many Tokens Is My Prompt? Counting and Why It Matters
- Context Windows Explained: Why Bigger Is Not Always Better
- Chain-of-Thought Prompting: When It Helps and When It Wastes Money
- What Is RAG? Grounding AI in Your Own Documents
- What Are AI Agents? The Loop, the Limits, and the Hype
- What Are MCP Servers? The Standard for Connecting AI to Your Tools
- How to Become a Prompt Engineer in 2026 (Honestly)
- n8n and AI: Build Your First Real Automation
- The 2026 AI Learning Roadmap: What to Learn, in What Order
- Context Engineering vs Prompt Engineering: What Actually Changed
- How to Evaluate AI Output: Building Evals That Catch Real Problems
- How to Cut Your AI API Costs by 70% Without Changing Models
Glossary
- Token
- Tokenizer
- Context window
- Prompt
- Prompt engineering
- System prompt
- Temperature
- Top-p (nucleus sampling)
- Zero-shot prompting
- Few-shot prompting
- Chain-of-thought prompting
- Tree-of-thought
- Self-consistency
- Meta-prompting
- Prompt chaining
- Structured output
- Function calling
- Large language model (LLM)
- Transformer
- Attention
- Parameters
- Reasoning model
- Multimodal model
- Open-weights model
- Fine-tuning
- LoRA
- Quantization
- Distillation
- RAG (Retrieval-Augmented Generation)
- Embedding
- Vector database
- Chunking
- Semantic search
- Reranking
- Knowledge cutoff
- Grounding
- AI agent
- ReAct pattern
- MCP (Model Context Protocol)
- Tool use
- Guardrails
- Human in the loop
- Inference cost
- Prompt caching
- Latency
- Streaming
- Determinism
- Evaluation (evals)
- LLM as judge
- Hallucination
- Prompt injection
- Lost in the middle
- Model drift
- Confidence score
- Bias
- Citation
- JSON Schema
- Cosine similarity
- Context engineering
- Mixture of experts (MoE)
Prompts
- Blog Post Outline Generator
- Tone Rewriter
- Cold Email Opener Generator
- Competitor Positioning Snapshot
- Meeting Notes to Action Items
- SQL Query Explainer
- Code Review Pass
- Weekly Automation Digest
- RSS-to-Social Repurposer
- Ad Angle Brainstorm
- Product Photo Prompt (Studio Style)
- Brand Mood Board Prompt
- YouTube Video Idea Generator
- Fact-Check This Claim
- Daily Priority Planner
- The 5-Part Prompt Framework — ChatGPT
- The 5-Part Prompt Framework — Claude
- Zero-Shot vs Few-Shot Prompting — ChatGPT
- Zero-Shot vs Few-Shot Prompting — Grok
- Chain-of-Thought Prompting — ChatGPT
- Chain-of-Thought Prompting — Claude
- Structured Output (JSON & Tables) — ChatGPT
- Structured Output (JSON & Tables) — Hugging Face (open-source)
- ChatGPT vs Claude vs Perplexity vs Grok — Perplexity
- ChatGPT vs Claude vs Perplexity vs Grok — Grok
- Role + Constraints to Kill Vagueness — ChatGPT
- Role + Constraints to Kill Vagueness — Claude
- Ask-Before-Answer for Ambiguous Tasks
- Self-Grading With a Rubric
- Prompt Chaining Architectures
- Meta-Prompting: Prompts That Write Prompts
- Evaluating Prompts Like an Engineer
- Project: A 3-Step Prompt Chain for Your Real Work
- Critique-and-Revise Loops — ChatGPT
- Critique-and-Revise Loops — Claude
- The ReAct Pattern (Reason + Act)
- Giving Loops Memory
- Planner–Worker–Reviewer Teams
- Loop Budgets and Failure Handling
- Project: Draft–Critique–Revise Content Machine
- Connecting an LLM to n8n
- Auto Content Repurposing Pipeline
- AI Email Triage & Auto-Responder
- Error Handling & Idempotency in n8n
- Human-in-the-Loop Approval Gates
- Project: Your First Production n8n Workflow
- Finding Your Audience's Real Language
- AIDA and PAS Prompts — ChatGPT
- AIDA and PAS Prompts — Claude
- AI-Assisted Audience Segments
- AI-Assisted Positioning Statements
- Full-Funnel Message Mapping
- Project: Research → Position → Launch Assets
- Generating High-Variance Ad Sets — ChatGPT
- Generating High-Variance Ad Sets — Grok
- Reading Ad Performance Data with AI
- Creative Testing Systems (Not One-Off Tests)
- Landing Page ↔ Ad Message Match
- Project: Design, Launch (or Simulate) and Read a Test
- Killing the "AI Voice"
- Structuring Image Prompts
- One Article, Five Formats
- Building a Reusable Brand Voice Prompt
- Batch-Creating a Week of Content
- The Two-Pass Edit: Structure Then Line — Claude
- The Two-Pass Edit: Structure Then Line — Claude (2)
- Original Insight: The Thing AI Can't Fake
- Project: One Pillar Piece + Four Repurposed Assets
- Hook-First Script Structure
- Title & Thumbnail Text Pairing — Claude
- Title & Thumbnail Text Pairing — Perplexity
- Reading Your Analytics with AI
- Scripting for the Retention Graph
- Series & Format Design
- Project: One Video, Fully Engineered
- Mining Keywords and Search Intent — ChatGPT
- Mining Keywords and Search Intent — Perplexity
- Competitor Content Gap Analysis
- Titles, Metas & Headings That Rank
- Getting Cited by ChatGPT & Perplexity — Perplexity
- Getting Cited by ChatGPT & Perplexity — Claude
- What RAG Actually Does
- Chunking: Why Split Size Matters
- Custom GPTs & Claude Projects
- Writing Docs That Retrieve Well