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A PM Cut PRD Writing from 2.5 Hours to 50 Minutes by Giving an AI the Team Rulebook

By zhouzhouya ·
Read original on juejin.cn ↗ Google Translate ↗ Alt translation

The bottleneck in AI-assisted PRD writing isn't generation speed; it's the gap between a generic model's output and a team's internal conventions. Embedding those conventions directly into the tool's instructions eliminates the half-hour of reformatting and gap-filling that usually follows a quick first draft.

Summary

A product manager turned TRAE Work into a standardized PRD generator by feeding it the team's templates, field definitions, and interaction rules. Instead of prompting from scratch, the workflow processes raw meeting transcriptions and bullet points through a fixed instruction set that enforces a seven-section document structure, distinguishes must-haves from optional optimizations, and auto-generates a list of missing information that needs confirmation.

The result compressed a typical 2.5-hour PRD cycle—material wrangling plus writing—to roughly 50 minutes: 20 minutes of preprocessing and 30 minutes of generation plus human fine-tuning. The same instruction-switching pattern extends to user research reports, competitive analyses, release notes, and post-mortems.

Three practical rules emerged: keep 2–3 prompt templates on standby rather than rewriting instructions, split large requirements by module to avoid context-window confusion, and treat the AI as an 80% draft machine—the remaining 20% of logic verification is where the PM's judgment still carries the load.

Takeaways
Pre-loading team templates, field definitions, and interaction rules into TRAE Work produced drafts that matched internal conventions on the first pass.
A fixed seven-section PRD structure—covering overview, pain points, detailed requirements, interaction rules, non-functional needs, dependencies, and acceptance criteria—was baked into the instruction set.
Mandatory output of a 'to-be-confirmed list' forced the AI to flag missing information rather than hallucinate it, exposing risks before review.
Total PRD time dropped from 2.5 hours to roughly 50 minutes: 20 minutes for material prep, 30 minutes for generation and human fine-tuning.
Keeping 2–3 reusable prompt templates for different requirement types (backend, C-end, campaigns) avoided rewriting instructions from scratch.
Splitting large requirements by module before feeding them to the AI prevented context-window overload and logical drift.
The PM treated the AI as an 80% draft engine; the final 20%—business-rule verification, terminology correction, and logic checks—remained a manual step.
Conclusions

The real efficiency gain isn't the AI writing faster; it's that the AI stops producing generic output that requires extensive rework to match team standards. The instruction set acts as a compliance layer.

Mandatory 'to-be-confirmed' output inverts the typical AI risk: instead of the model quietly fabricating details, it's forced to surface its own ignorance, which turns a hallucination problem into a review checklist.

The 80/20 framing—AI as draft engine, PM as logic verifier—is a more honest division of labor than the 'one-click final draft' promise that most AI writing tools imply but never deliver.

Concepts & terms
PRD (Product Requirements Document)
A formal document used in software development that defines what a product or feature should do, including business context, functional requirements, interaction rules, edge cases, and acceptance criteria. It serves as the source of truth for design, development, and QA teams.
TRAE Work
An AI-powered workspace tool by ByteDance that allows users to define custom instructions and workflows for document generation, code assistance, and other productivity tasks. It supports importing materials and running structured, repeatable tasks.
GIGO (Garbage In, Garbage Out)
A principle stating that the quality of output from a system is determined by the quality of its input. In AI-assisted writing, poorly structured or incomplete source materials will produce unreliable drafts regardless of model capability.
From the discussion

The exchange centers on AI's role in eliminating grunt work. Both comments agree that automation handles repetitive document tasks, but the first adds a sharper distinction: AI clears distractions without replacing human judgment on decisions, business pain points, or nuanced communication.

AI automates tedious, repetitive document integration, freeing time for higher-value work.
AI does not make decisions; it removes noise so humans can focus on business pain points and communication details.
Featured comments
六月暴雪飞梨花

AI really does solve tedious, repetitive work — automating the drudgery of document integration frees up more energy to dig into business pain points and communication details. AI won't make decisions for you, but it can clear away the noise.

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