1:36:30
Full Walkthrough: Workflow for AI Coding — Matt Pocock
AI Engineer
Overview
This video explains a structured workflow for using AI in software development, emphasizing that traditional software engineering principles remain crucial when working with AI. It introduces concepts like the AI's 'smart zone' and 'dumb zone' related to context window limitations, and the importance of breaking down large tasks. The workflow involves a 'grilling' phase for alignment, creating a 'destination document' (PRD), and then a 'journey' plan using a Kanban board for parallelizable, AI-driven implementation. The goal is to leverage AI for efficient coding while maintaining human oversight and control over critical decisions.
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Chapters
- LLMs have a 'smart zone' where they perform best, which is limited by context window size.
- As context grows, attention relationships become strained, leading to a 'dumb zone' where LLMs make poorer decisions.
- The 'smart zone' is roughly around 100k tokens, though this can vary.
- Tasks should be sized to fit within the LLM's smart zone to ensure optimal performance.
Understanding these limitations helps developers manage AI's capabilities and avoid common pitfalls, leading to more effective AI-assisted development.
Adding too many tokens to an LLM's context is like adding too many teams to a football league, quadratically increasing the complexity and straining attention relationships, causing the AI to become 'dumber'.
- An LLM session typically follows stages: system prompt, exploration, implementation, and testing.
- LLMs have a 'Momento'-like memory; clearing context resets them to the base state (system prompt).
- Compacting conversation history reduces token usage but doesn't fundamentally change the LLM's state reset behavior.
- Optimizing for frequent context resets (clearing) can lead to more consistent AI performance.
Recognizing how LLMs manage session state and memory is key to designing workflows that prevent information loss and maintain AI focus.
Clearing the context window in an LLM session is like starting a new conversation from scratch, losing all prior context and returning to the initial system prompt.
- The 'Grill Me' skill is used to achieve shared understanding and alignment with the AI, countering the 'specs to code' anti-pattern.
- It works by relentlessly asking questions about a plan or brief until a shared design concept is reached.
- This process creates a valuable 'conversation history' asset that documents the design decisions.
- Sub-agents can be used to explore codebases or perform tasks, reporting summaries back to the main agent, without significantly increasing the orchestrator's token usage.
This skill ensures the AI and developer are on the same wavelength, preventing misalignment and improving the quality of AI-generated plans and code.
When given a client brief about adding gamification, the 'Grill Me' skill asks questions like 'What actions earn points and how much?' to collaboratively define the feature's specifics.
- After the 'grilling' phase, a Product Requirements Document (PRD) is created to serve as the 'destination document'.
- The PRD summarizes the agreed-upon design concept, including problem statements, solutions, user stories, and implementation/testing decisions.
- This document acts as a clear target for the subsequent implementation phase.
- The PRD should include an 'out of scope' section to document rejected ideas or future considerations.
A well-defined PRD provides a clear roadmap and objective, ensuring that the AI-driven implementation efforts are focused and aligned with the project's goals.
A PRD for gamification might include user stories like 'As a student, I want to earn points for completing lessons so that I feel motivated' and define implementation decisions like 'Points are not retroactive'.
- A Kanban board is used to break down the PRD into actionable, independently grabbable tasks (issues).
- Tasks should be structured as 'vertical slices' of functionality, crossing all system layers (e.g., database, API, frontend) for early feedback.
- This contrasts with 'horizontal slices' which complete one layer across all features before moving to the next.
- Vertical slices allow for parallelization and enable AI agents to work more efficiently and provide near-instant feedback.
Structuring the implementation plan with vertical slices and a Kanban board optimizes the AI's workflow, enabling parallel processing and continuous feedback loops.
A vertical slice for gamification might involve updating the schema, creating a points service, and displaying points on the dashboard, all within a single, testable unit of work.
- The implementation phase can be largely automated, becoming an 'Away From Keyboard' (AFK) task for humans.
- A script (like 'Ralph Loop') orchestrates AI agents to pick up tasks from the Kanban board and implement them.
- This involves providing the AI with the codebase, the backlog of issues, and a prompt guiding its work.
- The process often uses a Docker sandbox for safe execution and can be run sequentially or in parallel.
Automating the implementation phase allows developers to focus on higher-level tasks like planning and review, while AI handles the bulk of coding.
An AFK agent script takes issues from a markdown file backlog, explores the repo, uses TDD to complete a task, and then potentially runs feedback loops, all without direct human intervention during execution.
- The role of the software engineer shifts towards planning, alignment, and code review as AI takes over implementation.
- Understanding codebases deeply remains critical, as a 'garbage codebase' will yield 'garbage out' from AI agents.
- While AI excels at implementation (AFK tasks), critical decisions and alignment phases require human involvement ('human in the loop').
- The volume of code review may increase as AI generates more code, requiring developers to adapt their workflows.
Adapting to these changes ensures developers can effectively leverage AI tools, enhance their productivity, and maintain control over software quality.
Even with AI generating code, developers must still review pull requests, ensuring the AI's output meets quality standards and architectural requirements.
Key takeaways
- AI development workflows benefit from applying traditional software engineering principles like task decomposition and clear planning.
- Understanding LLM context window limitations (smart vs. dumb zones) is crucial for effective AI prompting and task management.
- The 'Grill Me' skill is essential for achieving alignment and a shared understanding between developers and AI agents.
- A Product Requirements Document (PRD) serves as a vital 'destination document' that clearly defines the project's goals.
- Breaking down work into 'vertical slices' on a Kanban board optimizes AI implementation by enabling parallel processing and early feedback.
- The implementation phase can be largely automated using 'AFK' agents, freeing up human developers for planning and review.
- Deep understanding of codebases and architectural principles remains paramount for successful AI-assisted development.
Key terms
Smart ZoneDumb ZoneContext WindowAttention RelationshipsSystem PromptCompactingGrill Me SkillDesign ConceptSub-agentProduct Requirements Document (PRD)Kanban BoardVertical SliceHorizontal SliceAFK (Away From Keyboard) TaskHuman in the LoopRalph Loop
Test your understanding
- How does the concept of an LLM's 'smart zone' and 'dumb zone' influence how you should structure tasks for AI?
- Why is the 'Grill Me' skill considered a counter-pattern to 'specs to code', and what problem does it solve?
- What is the difference between a 'vertical slice' and a 'horizontal slice' in task planning, and why are vertical slices preferred for AI implementation?
- How does the workflow described shift the role of the human software engineer from direct coding to other responsibilities?
- Explain the purpose of a PRD and a Kanban board in the AI-assisted development workflow presented.