Most AI coding problems don't start during implementation — they start in the spec. In this episode, Dex (HumanLayer) and Vaibhav Gupta (Boundary, creator of BAML) walk through a new approach for building with AI: separating product design from technical design before a single line of code gets written.

Instead of treating specs as a formality, they show how detailed product discussions, technical design docs, and program design dramatically improve the quality of AI-generated code. The goal isn't to review more code — it's to make code review easier by making better decisions earlier in the process.

Along the way: how HumanLayer evolved its workflow from "research, plan, implement" into separate product and technical design phases, how mockups and rendered artifacts help surface problems before implementation, and why moving cognitive burden upstream creates tighter feedback loops and more reliable outcomes.

KEY TAKEAWAYS
Better specifications lead to better implementations. Separating product design from technical design creates more leverage at each stage. Design docs become more valuable when treated as artifacts the model and humans both read, not one-time requirements. Giving agents a measurable definition of success lets them propose improvements over time. Rich visual mockups surface problems earlier than text-only specs. HTML and Markdown serve different purposes — one for humans, one for token-efficient model input. Moving verification earlier in the pipeline reduces the need for expensive code review later. Standardizing what gets checked (like flagging deviations from the plan) makes AI systems easier to trust.

SUMMARY
Dex and Vaibhav unpack how their workflows evolved past the standard research-plan-implement loop. By splitting design into separate product and technical phases, they show how AI coding agents can arrive at better outcomes with fewer surprises during implementation.

Using a live redesign of the HumanLayer task page as the working example, they walk through mockups, a product spec, a technical design doc, and program-level architecture — while discussing what makes an artifact actually useful to both humans and models. The conversation also covers the tradeoffs between HTML and Markdown, why CLAUDE.md files tend to rot, and how richer, more visual representations can improve collaboration between PMs, engineers, and the model itself.

By the end, the theme is consistent: the goal isn't to catch mistakes later — it's to make enough good decisions early that implementation becomes predictable and review becomes easy.

LINKS
Website: https://www.boundaryml.com
Docs: https://docs.boundaryml.com
GitHub: https://github.com/BoundaryML/baml

TIMESTAMPS
00:00 Cold Open: Using AI to Build Better Product Specs
00:26 How HumanLayer's AI Workflow Has Changed
01:32 Intros & Catching Up
05:26 From Research-Plan-Implement to Product + Technical Design
07:09 Why Product Design and Technical Design Need to Be Separate
08:53 Less Clarity, More Leverage: The Three Zoom Levels
09:57 How HumanLayer's Team Already Splits This Work
12:38 Why Product Thinking Is a Different Skill Than Engineering
13:48 Applying Product Thinking to the HumanLayer Task Page
15:43 Giving AI a Way to Measure Success
17:10 Prompting for Creativity & Better Specs
20:08 Why Seeing a Mockup First Creates Buy-In
22:30 Choosing the Right Artifacts for AI Systems
24:19 HTML vs. Markdown for Human-Facing Docs
29:13 Rich Formats vs. Token Efficiency
32:35 Making This Work for Less Technical Teams
34:54 How HumanLayer Invents and Evolves New Workflows
37:00 Catching Deviations & Moving Verification Earlier
42:16 The Problem With Giant CLAUDE.md Files (and a Better Fix)
48:34 Building Workflows That Scale With AI

TOPICS COVERED
Product specifications, AI coding agents, HumanLayer, BAML, PRDs, technical design documents, program design, product design, agent workflows, Markdown, HTML, mockups, Claude Code, CLAUDE.md, software engineering

HASHTAGS
#ClaudeCode #AIEngineering #CodingAgents #AIThatWorks #SoftwareEngineering #ProductDesign #DeveloperTools #HumanLayer #BAML