Debugging with AI
Narrow hypotheses, gather the right signals, and prompt in layers instead of blasting the model with noise.
Not a chatbot. A structured playbook for engineers.
Stop asking AI the wrong questions when debugging code.
A structured playbook that teaches engineers how to break down problems, ask better questions, and solve issues faster using AI.
The gap most teams miss
Why engineers struggle with AI
-
01
Vague questions lead to useless answers
-
02
AI outputs are hard to trust in real systems
-
03
Engineers don’t know what context to provide
-
04
Debugging becomes trial-and-error prompting
The problem is not AI. It’s how you ask.
How it works
A structured way to think with AI
This is a system of step-by-step workflows for real engineering problems—not open-ended chat. Each playbook focuses on asking the right questions in the situation you’re in, so you move from guessing to isolating root causes and real options.
Building features with AI
Decompose scope, define contracts and edge cases first, then use AI for bounded, reviewable slices of work.
Investigating production issues
Time-boxed triage paths that pair telemetry, recent changes, and targeted prompts.
See the shift
Example: Debugging a slow API
Bad question
“Why is my API slow?”
Better question
“Given logs, architecture, and metrics, what are likely bottlenecks and how do I isolate them step-by-step?”
The better prompt names the evidence you can supply, asks for hypotheses ranked by likelihood, and forces a narrow investigation path—so AI suggestions stay grounded and you can validate each step in your real stack.
Pricing
Simple access while we’re in beta
Starter
$9–$19
one-time · or free beta early access
- Core playbooks & workflows (static for MVP)
- Debugging, features, and production triage tracks
- Works with any AI tool you already use
Stripe Checkout: connect your live or test link here when ready.
Pro · coming soon
$10–$20 / month
Deeper collections, updates, and team-ready formats.