Where AI saves time in design and content work, and where it doesn't
The AI Product work group on design exploration, prototypes, first drafts and the slow parts, with examples from Webflow, Snap and a 48-hour hackathon build.
Updated
AI makes exploration and first drafts fast: design variations in minutes, throwaway prototypes, a site from one prompt. The slow parts are now judging which draft is good and making that draft match your product.
- Explore more directions. Sachin Rekhi asks prototyping tools like Bolt and Magic Patterns for several layouts at once and combines the best parts. Cheap prototypes let a team build several, test them and keep the winner. He suggests one baseline prototype with the product's functionality and styles, so new work matches the product from day one.
- First drafts come fast. Rachel Wolan has Claude interview her about her week, research the last two weeks and draft a 1,000-word post that used to take her three or four hours. By early 2026, 22%+ of new Webflow sites started in its AI Site Builder. Sharmeen Browarek Chapp and a co-builder shipped Bukkit, a social bucket list app, in under 48 hours at a Lovable hackathon.
- Keep AI where the work happens. Shishir Mehrotra says the "AI detour" of switching tools, prompting and pasting results back eats the time the model saved. Rachel's team put inline copy generation inside the Webflow editor for the same reason.
- Judging and scope stay slow. With 10 AI drafts, someone still has to pick the good one and explain why, which Shishir calls taste. Sharmeen adds scope: when every fix feels like one prompt, hours go to polish. She would plan for ramp-up time, since her team's gains came later in the build as prompts got sharper.
- Question the old defaults. Jack Brody, drawing on his Snap days, suggests teams question old conventions before using AI's speed to try many approaches. He says capable models often let a team build the breakthrough first and then work out which problem it solves, so designers spend more time productizing new capabilities than executing a set spec.
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Frequently Asked Questions
What should a first eval setup for an LLM product include?
A sheet of 50 to 100 real outputs graded by people with notes, a rubric checked against customer evidence, a fixed bug set and rule checks in code, and an agreed list of failures that block a launch.
How do you know your LLM evals measure what users care about?
Build eval cases from failures users hit on real tasks, then compare scores with what people do next: rephrasing the question, editing heavily, abandoning the answer or spending time checking it.
Which parts of a product should become agentic first?
A frequent, bounded job inside a workflow people already use, where they can review and correct the result before it goes out. Prototype it on real inputs before committing to the full agent.
How can a working professional build AI skills that last?
Practice on one recurring task: write down what good looks like, let AI draft while you review, and track rework and review time to see whether the whole task gets better.
How is AI changing the product manager's job?
Execution and coordination get cheaper, so more of the job is choosing the problem, judging output and supervising agents. Sachin Rekhi still wants PMs talking to customers themselves, and Ami Vora wants them close to those conversations.
Which operations work can AI agents handle today?
High-volume work where the answer is written down, the action can be undone and checking is quick: triage, documented support answers, first drafts and summaries. Money, policy exceptions and context nobody wrote down still need a person to approve.
How will AI change services businesses?
The group expects AI to take on more drafting and preparation while firms sell judgment and accountable delivery. Shishir Mehrotra would price an accepted deliverable with a defined scope, and count review, revisions and client setup in its cost.
Why doesn't AI adoption show up in business results more often?
Usage is an input. Results come from changing one workflow and measuring its outcome, including the time spent checking and fixing AI output and whether a decision changed.
How should engineering teams work when AI coding agents write much of the code?
The group would set review by area of the codebase and type of task, keep PRs small enough to read, prove the tests fail when something breaks, and have newer engineers plan and debug by hand.
