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Aakash GuptaIf you can’t AI prototype after this, nothing will help you
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At a glance
WHAT IT’S REALLY ABOUT
From AI slop to high-craft prototypes with validated solutions fast
- Anthropic’s approach flips traditional roadmapping by prototyping many problem-solution pairs first, dogfooding them, and only then productionizing the best-performing prototypes.
- “AI slop” happens when prototypes are generic, undifferentiated, and shallow in real workflows, but high-craft outcomes are achievable with the right techniques.
- Rekhi’s AI Prototyping Mastery Ladder outlines 15 skills from apprentice (prompting/editing/design consistency) to journeyman (debugging/versioning/diverging/validation) to master (functional prototypes and product shaping).
- Core methods include baselining your existing product via screenshot recreation, iterating with targeted edits (including batching), and forking templates to ensure consistent design across prototypes.
- Master-level validation uses deployed prototypes with embedded surveys plus analytics (e.g., PostHog events, heatmaps, session replays) to scale learning and simplify interfaces based on real behavior.
IDEAS WORTH REMEMBERING
5 ideasPrototype many problem-solution pairs before committing roadmap capacity.
Anthropic-style “product shaping” prioritizes solutions that are already internally or customer-vetted, reducing the risk of building the wrong thing even when the problem is real.
Treat “one-shot apps” as a starting point, not a shippable output.
The first AI-generated UI is often generic in styling, undifferentiated in concept, and weak on real user workflows—use it to accelerate iteration, not to declare victory.
Baseline your product’s look-and-feel, then build everything on top of it.
Recreating a screenshot, refining it through edits, and duplicating/forking that baseline lets future prototypes inherit components and styling automatically, eliminating “wireframe AI” aesthetics.
Batch related edits to reduce round-trip time—without losing control.
Grouping similar changes (e.g., multiple color tweaks) speeds iteration, but batching unrelated changes makes it harder to isolate failures and manage versions when something breaks.
Use diverging intentionally—and use multiple tools to expand the idea space.
Ask for multiple variants (and even run the same explore prompt in different tools) because differing system prompts yield meaningfully different designs, producing more inspiration than a single tool run.
WORDS WORTH SAVING
5 quotesThey’re prioritizing not only what is actually… a problem worth solving, but a problem-solution pair that’s already vetted.
— Sachin Rekhi
It still is AI slop because we could never ship this. This would never be considered high-craft work.
— Sachin Rekhi
There’s actually 15 unique skills you kind of have to master to be able to do AI prototyping well.
— Sachin Rekhi
We should be using [AI] to create multiple outputs… a designer would come up with three variants.
— Sachin Rekhi
If a PM is trying to get a full version of their product out through these vibe coding tools, they’re doing it wrong.
— Sachin Rekhi
SPEAKERS
Aakash Gupta
hostHost and product leader who publishes a newsletter and product/AI resources (bundle.aakashg.com).
Sachin Rekhi
guestFormer Head of Product for LinkedIn Sales Navigator and educator on AI prototyping for product teams.
Aakash Gupta
hostHost voice used for brief interjections and a Reforge sponsor read using the vanity URL reforge.com/aakash.
High quality AI-generated summary created from speaker-labeled transcript.
