Chronology

• Start Here: What this site is
• How to read it: history first, reference later
• The 10 Prompt Patterns You’ll Reuse

Part I — Origin Story and Base-Model Prompting

• How I Became OpenAI's First Prompt Engineer
• The Evolution of Prompts: From Completion to Systems
• Base Models vs Post-Training: What Each Layer Does
• Model Identity and Statelessness: Why Explicit Context Matters
• The Stateless AI Guessing Game: A Prompting Lesson in Memory
• GPT Demo Set List: Early Prompt Patterns That Still Hold Up
• Early Sentence-to-Email Prompts: A Foundational Transformation Pattern
• GPT-3 Emoji Story Demo: Narrative Compression in Tokens
• Separating Instruction from Content: A Core Prompt Reliability Pattern
• Magic Words in Prompting: Domain Terms That Steer Behavior
• Invoking Experts in Prompts: When Persona Framing Improves Results
• GPT-3 Grammar and Style Editing in Practice
• Magic Phrases for Moderation: Prompt Patterns That Improve Safety Calls
• GPT-3 for Regex, Bucket Policies, and Solidity Tasks
• Rethinking best_of in GPT-3: Why It Misleads
• The Fifth-Grade Summary Moment: Audience-Aware Compression
• Interlude: Prompting as pattern design (why formatting beats adjectives)

Part II — Reliability and Control Patterns

• Mini Prompts for Trick Questions and Nonsense Inputs
• Prompts to Reduce Hallucinations: Practical Control Patterns
• Cross-Temperature Hallucination Testing for Sanity Checks
• Temperature in LLMs Explained: What It Actually Controls
• Seeded Creativity for LLMs: Controlled Randomness That Helps
• Creating Better Quiz Distractors with LLMs
• Bracketing Letters for Wordle: Token-Level Prompt Control
• The Missing Bracket: How Tiny Formatting Errors Break Outputs
• Prompt Repetition and Rephrasing: A Reliability Tactic That Lasts
• Interlude: When prompt engineering became an engineering problem

Part III — Efficiency: Cost, Compression, and Small Models

• Prompt Size Reduction Checklist: Cut Tokens Without Losing Quality
• Small Model Advantages: When Smaller LLMs Outperform Bigger Ones
• Small Models, Big Knowledge: Prompting Past the First Guess
• Using Small Models for Complex Natural-Language Tasks
• Large Text Pattern Analysis with Prompted Models
• Prompt Maker: How to Teach Prompt Patterns by Example
• Compute at Scale: Growth, Limits, and AI Demand
• How Small Can AI Be? Practical Limits and Opportunities
• Interlude: The cost curve changes everything

Part IV — Context and Scaffolding Workflows

• Context as an AI Lever: The Compounding Effect of Longer Windows
• Small Capabilities, Big Ramifications in Prompt Design
• Scaffolding Long-Form Content: Prompt Patterns for Coherence
• Radio Play Scaffolds: A Better Prompt Pattern for Story Generation
• Building AI Choose-Your-Own Adventures with Prompt Scaffolding
• Character-Threaded Summarization for Long Documents
• Memory in Conversational AI: Why Context Persistence Matters
• Outcome-Oriented Prompting: Define Success, Then Generate
• Style Guides for AI Writing: Getting a Specific Voice
• Crystallized vs Fluid Intelligence in Language Models
• Personal AI Evaluation Methods for Real-World Quality
• Interlude: From ‘write me a thing’ to outcome specs + evals

Part V — Retrieval and Grounding Systems

• Understanding Embeddings for Better Prompting and Retrieval
• Embedding-Based Retrieval Strategies That Actually Work
• Context vs Retrieval: A Practical Decision Framework
• Grounding Prompts with Wikidata and SPARQL
• The Prompt Context Flywheel for Continuous Improvement
• Interlude: Prompting becomes a system: retrieval + transformation + reuse

Part VI — Fine-Tuning as an Adaptation Layer

• Fine-Tuning Fundamentals: When to Use It and When Not To
• Fine-Tuning Methods Guide: SFT, DPO, and Beyond
• Cost Savings via Fine-Tuning Smaller Models
• Model-Assisted Data Preprocessing for Better Fine-Tuning
• Interlude: The prompt is now a workflow spec

Part VII — Products, Multimodal Systems, and the Frontier

• GPT Tools: Fast Prototypes, Real Constraints, and Shipping
• Discovering Useful Libraries with AI Coding Prompts
• Code Refactoring with GPT-3: Practical Prompt Patterns That Work
• Tool Makers vs Tool Users: Where Product Value Actually Lives
• Lessons from an Ambitious AI Build
• Why I Didn't Launch AI Channels
• Hackathons and Model Capabilities: What Fast Experiments Reveal
• GPT-4 Vision Refrigerator Demo: A Practical Multimodal Moment
• Localization Techniques for Vision Models in Real Workflows
• Vision Models at the Frontier: What Changed and Why
• Big and Small Models in Robotics: A Hybrid Architecture
• The Uneven AI Frontier: Why Capabilities Arrive Jagged
• The Frontier Is Wider Than It Looks
• Challenging AI Paper Claims with Practical Replication