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Reviewing AI-drafted PRDs
An r/ProductManagement post asked a practical question. When Claude or ChatGPT drafts a spec, how do engineering, design and leadership review it, and how do their changes get back into the source? The replies agreed on one point: the reviewed document lives in a tool people can read and comment in, not in the chat.
- Write first, then use AI as a critic. One PM writes the PRD by hand in Confluence so they don't "outsource my thinking." Then a dedicated Rovo agent checks it for structure, missing scenarios, weak assumptions, risks and edge cases .
- Human gate plus a ticket trail. Another PM reviews and usually rewrites the AI output, which they rate as about 97% accurate. They then assign a functional lead to review it second. Comments go on the ticket, a new version follows, and the result is broken down in sprint planning .
- Don't sync every comment back automatically. One commenter has the AI draft against a single human-readable document. Comments stay open until the owner accepts them, and only then does the agent update the doc and show a diff. Otherwise feedback "turns into scope before anyone has made the call" .
- Agent summaries. One team gives each opportunity brief its own Slack channel. An agent fleet summarizes the disagreements and changes at the end of each day .
- Counterpoint: reviewers often aren't reading PRDs anyway. They paste them into AI. So tell the story with a prototype and keep a shorter document explaining why and what .
Agents in production create new work
Hiten Shah asked who will be "the IT person for everyone's AI agents" when a paid-for agent "quietly stops doing its job" a month later. His bet: agent builders may create more demand for support than they remove . In an r/startups thread about agent infrastructure, commenters made a similar point. Observability and orchestration are crowded, and the gaps are in permissions, audit trails and cost control. Those needs show up only in production and are best found by talking to teams that already deploy agents . Two specific discovery prompts came up. First, how do teams reconcile a CRM write when the tool call times out and a retry might create a duplicate ? Second, why did teams that evaluated tools decide not to buy: security review, data residency, integration, or no budget owner ?
At a Masters of Scale debate, Signal's Meredith Whittaker said a consumer agent that books a party needs contacts, payment, browser, calendar and messages, and that following instructions found in text makes prompt injection serious. She cited cases involving Copilot, Perplexity's agentic browser and Google Calendar . Her conclusion was that trust has to come from verification . Her opponent answered that risk depends on the agent, the use and the access requested. He mostly lets agents read online rather than write , and he advises small steps, tiered access and verification . For PMs building agents, the design takeaway is to scope permissions narrowly and disclose clearly what data the agent touches.
Product Hunt opens to agents
Product Hunt released a version where builders can launch via MCP and find competing products. Operators can search more than 500,000 apps, and investors can filter startups with a proprietary model that predicts Series A likelihood . Shah noted that nearly 25,000 makers launched on Product Hunt in October. That fits what he told Ryan Hoover in 2019: shipping is getting easier and making a product work is getting harder .
Discovery hygiene
- Idea repos go stale because nobody revisits them, whatever the tool. Give each idea a revisit date and review it in an existing planning cycle. Archive any idea that comes up twice with no new evidence . One team organizes the repo by problem, with quotes and tickets as evidence underneath. Duplicates merge on their own, and planning becomes ranking problems by evidence . Another adds a one-line success criterion to each idea, which later becomes the acceptance criteria .
- Your ICP is a guess until customers prove it. One founder built for B2B SaaS, but only 1–2 of the first 10 paying accounts were SaaS companies. Small agencies made up the largest group . Rank segments by how deep the replies go , or by who agreed to a paid next step .
Careers
- One commenter cited their employer's study of 150 PMs at Meta, Google and Amazon. 42% switched company and role at the same time, 28% moved into PM internally, 23% came after postgraduate study and 7% came straight from undergrad. 64% had technical degrees and only 13% had a certification .
- Formal 30-60-90 onboarding seems to be fading. One commenter describes a week of meeting people, with contributions expected by week two or three .
- Lenny launched The Next 50, a quarterly list of early-stage startups ranked on headcount growth, funding and investor interest . To qualify, a startup must have been founded in 2023 or later, be at Seed or Series A, have raised at least $5M, have a top VC investor, and have 10–250 employees with headcount growing .
Also
One team that deploys continuously publishes changelog pages for version ranges, such as /changelog/v1.50.0..v1.54.2. Support can send a customer everything since the version where they reported a bug .