Matt Pocock

AI-engineering and TypeScript educator (AI Hero, Total TypeScript). Developer of structured agent execution workflows and phased engineering systems.

LLM Fundamentals Teaching

Through the LLM Fundamentals course (raw/aihero-video transcripts), Pocock teaches the foundational mechanics of LLM applications: the llm message protocol (stateless message histories, system prompts, token economics), the tool calling loop (with the under-6-tools rule of thumb), structured outputs, model provider abstraction via Vercel’s AI SDK, and model context protocol basics. His pedagogy is show-the-thing-first: demonstrate a working use case before the abstraction.

Core Operational Models

1. The Vicious Circle of AI Code Decay

“Bad code is now the most expensive it has ever been.”

• Unconstrained agents produce code quickly without deep boundary abstractions.
• Decayed codebases increase context noise and hallucination rates for subsequent agent turns.
• Solution: Enforce strict workflow phase boundaries, type safety, and automated test harnesses before writing feature logic.

2. The Idea-to-Ship Execution Graph

• Grilling: Sharpen intent and reject vague specifications upfront before code generation (see grilling doctrine and grill with docs).
• Handoff Artifacts: Compress conversational context into structured handoffs; clear context windows between phases.
• Tracer Bullets: Verify end-to-end integration slices early to prove system viability (see tracer bullets).

3. Progressive Skill Encapsulation

• Package workflows into discrete, composable skills loaded on-demand rather than polluting global instructions (see progressive disclosure).

Related

richard feynman, andrej karpathy, idea to ship flow, grilling doctrine, tracer bullets, context engineering, ai coding taxonomy.