1. Becoming AI native without giving up Figma

Going AI native at Razorpay doesn’t mean ditching Figma completely. Jain now uses Figma for visual note taking. He does a design audit and pulls together his 1-1 notes and competitor screenshots into a Figma file.

He then feeds the whole Figma file to Claude as the initial prompt to build prototypes!

2. Writing a design doc before prototyping

At Razorpay designers don’t directly start vibe coding. For every medium to large project Meena uses Claude first to write a design doc and align with cross functional stakeholders on the problem and business goals!

3. Presenting prototypes in user’s voice

When Meena presents her design work at Razorpay she doesn’t click through screens or just show AI prototypes. She walks the entire room through the whole flow in the user’s voice.

Meena won't say I made this button. She'll say I'm a merchant who woke up wanting to check my orders. So all the stakeholders feel the actual user pain points instead of just watching a demo prototype!

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1. Designing a new app in one week

Ezaz designed Swiggy's entire Snack app in a single week. Not just the concept but the whole thing - UI, animations, illustrations, error states, and branding.

The reason it was even possible is because he wasn't starting from scratch. Ezaz reused components from Swiggy's existing design system and tweaked them. Also engineers and designers were in the same room the whole time. So every screen got checked for feasibility on the spot. Also the cofounder Harsha reviewed the Figma files and prototypes himself which cut out the middle management delay!

2. Designing for Gen Z

Ezaz wanted Snacc to feel like something Gen Z was already in on and not a food app trying to look young. So he designed it around the things they actually share which is memes and most memes involve cats.

So he designed a whole brand around a ginger cat and also created delightful error states! My favorite error state is the one where the cat is cutting a cucumber when all the vegetarian food items are sold out!

3. Design hackathons to make AI images look real

Some dishes are just hard for AI to get right especially Indian food. The crispy rolled dosa was so tricky that the Swiggy design team turned it into an internal design hackathon to see who can nail it.

And the fix always comes down to the tiny design details like prompting for a single coriander leaf on top or ghee dripping down the side because that’s what makes AI food look real and appetizing!

4. AI won't replace designers

Ezaz strongly believes that AI will never be able to replace the thinking and the craft behind a designer’s work. He has an amazing analogy - he's the director of the movie and the tools are just the camera. The camera keeps getting better but you still need someone who knows how to direct.

His advice to worried designers is simple. Treat AI as just another tool and play with it in your free time so you're never behind. But keep your focus on the fundamentals of design. Jumping to the tool without the craft won't take you anywhere.

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1. Use Ask User Question tool in Claude

You can try to cram every detail into one giant prompt and one shot it. Danny doesn't.
He tells Claude to use the ask user question tool and lets it interview him instead. It pulls out the stuff he had in his head but never typed in the prompt. So all the little design details actually make it into the prototype!

2. Claude Design vs Claude Code

Danny picks his tool based on how sure he is about the idea. If he's exploring wide with throwaway concepts he uses Claude Design. If he's dialing in the details or actually shipping he uses Claude Code.

3. Don’t worry about token optimization

Wealthsimple takes a carrot approach and encourages the design team to use different AI tools and not worry about token optimization. The goal is to save time instead of tokens!

4. Use code review skill when pushing PRs

Before Danny tags an engineer on a PR he runs Wealthsimple's code review skill first. This Claude skill spins off eight sub-agents, each one hunting for something different like dead code, performance, security holes, best practices. It catches code issues so the engineers aren't stuck cleaning up his mess!

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1. Your PRD is the first AI prompt

Andy doesn’t waste time on figuring out the perfect prompt or one shotting. Instead he uses Miro AI to create a PRD based on his design discovery findings. Then he pastes that whole PRD straight into Magic Patterns as the initial prompt.

As a result the first prototype created using AI is already 60 to 70% of the way there!

2. Move your design system out of Figma

Maintaining the design system in Figma was a huge resource sink for the ESO design team. Five designers, no dedicated system owner, constant back and forth with engineering to keep Storybook in parity.

So Andy and the design team rebuilt the entire design system in Magic Patterns in a week. When a user tests one of Andy's prototypes now nothing feels off because the prototype uses production components.

3. Set up AI guardrails

Instead of re-prompting over and over for consistency Andy's team baked the healthcare standards, brand, UX laws and other context into Magic Patterns as rules.

This cuts the random nature of AI so the same prompt stops giving four different looking results. And it lets PMs and execs prototype without going outside the lines!

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1. Prototype Switcher

At CNN Jennifer Spriggs builds interactive prototypes in Cursor with a version switcher baked in so she can flip between different design options live during a design critique.

This saves her hours of building out different prototypes in Figma!

2. Async Design Critiques

At Ramp George Bugg runs async design critiques to cut down on the number of meetings. Designers drop vibe coded prototypes built using Magic Patterns into Slack and stakeholders leave feedback whenever it suits them.

He’s found that going async makes people focus way more on the actual design instead of just reacting live during a design review.

3. Critique Recaps

At Figma Kelly starts each design critique with a recap of the last one. She shares the high level decisions that were made plus the feedback she actually acted on since then.

It jogs everyone’s memory and makes people feel valued because they can see their feedback actually got implemented!

4. Silent Design Critiques

At Hex Adam Storr runs a silent design critique to prevent the loudest voice in the room from dictating the entire product experience.

It kicks off with a quick two to three minute overview. Then a FigJam timer runs for five to six minutes with some music playing while everyone goes heads down and leaves feedback using stickies. When the timer’s up the presenter walks through the notes and drives the discussion.

5. Design Critique Vibes

At Descript John Voss splits design critique into three separate reviews. One of them is just for the design team to go deep on the design details and craft.

The other two are where the bigger product strategy calls get made. He keeps them separate so important product and go to market decisions don’t creep into the design critique. Crit stays focused purely on improving the craft!

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1. Proactively find friction in the product

Clint doesn't wait for a PM to hand him a PRD. Every week he goes friction hunting on his own, looking for where users are getting stuck across onboarding, activation, and drop-off points.

Granola records his meetings so he can pull out the exact friction points stakeholders mentioned. Enterpret gives him the voice of the customer aka how users actually feel when using the product. And Hex shows him what users are actually doing in the product. Together these tools show Clint the friction points in the product experience!

2. Use AI to run growth experiments supafast

Clint designs growth experiments using two AI tools depending on the job. When he's inventing something brand new he goes to Claude Code so he can break away from the existing design system and explore freely. When he's refining a product flow he uses Magic Patterns.

And because his design system lives inside Magic Patterns now whatever he builds is production ready. He literally hands engineers a Magic Patterns link as the handoff which cuts out a ton of back and forth. So now Clint is able to ship new experiments in 2 weeks instead of a month!

3. Share context async with stakeholders

Once Clint has a prototype worth sharing he doesn’t schedule a meeting. He records a quick Loom walking through what he explored and why, then drops it into a Slack thread with the prototype links.

It turns into an async meeting that happens without a meeting. Stakeholders drop comments and questions when it suits them, and anything that needs a deeper discussion gets picked back up in his weekly design sync. Show and tell minus the calendar chaos. I think this is a genius strategy for stakeholder alignment!

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1. Understand how the agent thinks before prototyping

Carol didn't jump straight into pixels. She sketched a little diagram in Figma to map what the agent actually does. For example the agent gets context, does its thinking and crunching, then outputs something the customer can read without hunting for a button.

That mental model was her grounding moment. Once she understood how the agent operates she began vibe coding different ideas!

2. Design in production vs Figma/AI tools

Carol started in Figma and then vibe coded prototypes in Cursor but kept hitting a ceiling. Agentic experiences are non-deterministic so a prototype can only take you so far.

To really understand how the agent responds she had to design directly in production with the actual model and real data.

3. Agentic design principle

A key principle Carol designed around is intentional confirmation. The system asks follow up questions and drafts the transfer for you but it never transfers the money on its own.

You confirm the final action with a deliberate button press. This prevents customers from accidentally moving money to the wrong account or recipient!

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1. Using AI to go from 0-70%

George doesn’t touch Figma at all in the initial stages of the design process!

Instead he uses the AI prototyping tool Magic Patterns for exploring different design concepts. This helps him move super fast because he doesn’t have to worry about pixel perfectness or responsiveness!!

2. Sharing AI prototypes with stakeholders

George doesn’t share Figma files with product managers. Instead he shares AI prototype links which helps him get stakeholder alignment quickly.

He just drops the prototype links in Slack to reduce meeting fatigue. No unnecessary meetings. I think this is the future of design critiques!

3. Adding AI prototypes in Figma dev handoff file

George still follows the traditional engineering handoff process but with a small caveat. He adds the AI prototype links from Magic Patterns in the handoff page in Figma.

This lets the engineers click through the product experience and visualize the edge cases which in turn reduce follow up clarification meetings.

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