What are the key takeaways from “I stopped using /grill-me for coding. Here’s what I use instead:” on Matt Pocock?

Frequently asked questions about “I stopped using /grill-me for coding. Here’s what I use instead:”

What is "I stopped using /grill-me for coding. Here’s what I use instead:" about?

In "I stopped using /grill-me for coding. Here’s what I use instead:" (Matt Pocock, May 2026), generic prompting leads to hallucination and verbosity. By integrating Domain-Driven Design (DDD) principles—specifically ubiquitous language and ADRs—into an iterative AI 'grilling' session, you can achieve near-telepathic…

What does "Ubiquitous Language" mean in "I stopped using /grill-me for coding. Here’s what I use instead:"?

In "I stopped using /grill-me for coding. Here’s what I use instead:", Derived from Domain-Driven Design, this concept suggests that the language used in code, documentation, and conversation should be identical. By forcing the AI to stick to this glossary, you eliminate the friction of translating ideas into…

What does "Architectural Decision Record (ADR)" mean in "I stopped using /grill-me for coding. Here’s what I use instead:"?

In "I stopped using /grill-me for coding. Here’s what I use instead:", ADRs are essential for documenting decisions that are difficult to reverse. They provide the AI (and future human maintainers) with the context needed to understand why a specific approach was taken, preventing the accidental undoing of critical…

What does "Grill with Docs" mean in "I stopped using /grill-me for coding. Here’s what I use instead:"?

In "I stopped using /grill-me for coding. Here’s what I use instead:", This is an improvement over the original 'Grill Me' skill. It forces the AI to analyze your `context.md` file first, which ensures all recommendations are grounded in your specific project needs rather than generic programming advice.

What is this episode about?

Generic prompting leads to hallucination and verbosity. By integrating Domain-Driven Design (DDD) principles—specifically ubiquitous language and ADRs—into an iterative AI 'grilling' session, you can achieve near-telepathic alignment between your intent and the AI's output.

What are the key takeaways?

• Move from raw prompting to 'context-aware grilling' by forcing the AI to reference your local repository documentation. — Reduces the need to re-explain domain concepts, resulting in faster and more accurate code generation.
• Use Architectural Decision Records (ADRs) for non-obvious choices that are hard to reverse. — Prevents long-term technical debt by capturing the rationale behind trade-offs that simple code comments miss.
• Shared language is a performance optimization for AI agents. — Aligning on terminology significantly reduces token consumption in thinking traces and generated code.