For Product Managers

Your engineers ship
in hours now.Your PRD is still from last Tuesday.

AutoDoc DocGenerator connects to your codebase, GitHub, and Jira — and keeps every PRD, user story, and acceptance criteria automatically current with what's actually built. No manual updates. No stale specs.

Built forClaude Code teams🔁Agentic sprints📄Living PRDs🔗GitHub-native
PRD is stale
STALE

PRD: Bulk Export v2

Last synced3 weeks ago
Commits untracked47
Acceptance criteriaWritten from memory
Sections current1 of 4
Connected toGitHubJiraConfluence
⚠ 47 commits since last update — your spec is out of date

AutoDoc watches every commit and keeps your documentation in sync

The Velocity Gap

Engineering got 10× faster.PM process didn't move.

Claude Code ships features in hours. AI agents open PRs overnight. Your PRD is from last Tuesday and your grooming ceremony still runs like it's 2020. Something has to give.

Engineering velocity10× faster with Claude Code + Cursor
2022→ 2026
PM process adaptabilityBarely moved in 4 years
2022→ 2026

The gap between these two lines is where your team loses weeks, ships wrong features, and rewrites specs. AutoDoc closes it.

Right now

Engineering Reality

  • Feature: ticket → merged PR in 4 hours
  • Claude Code writes implementation, tests, edge cases
  • AI agents iterate automatically from CI feedback
  • Codebase complexity doubles every 6 months
  • Technical decisions made in real-time, in the AI session
📋
Still the same

PM Process Reality

  • ·PRD: 2–3 days to write, 1 week to review
  • ·Grooming: estimating stories nobody fully understands
  • ·Acceptance criteria written without seeing the real code
  • ·Sprint locked for 2 weeks regardless of what ships
  • ·"Done" means docs were supposed to be written
Where it breaks down

Three daily breakdowns every modern PM lives with.

Not generic "documentation challenges." Specific moments in the sprint where the velocity gap bites hardest.

01

Your PRD Was Obsolete Before the Sprint Started

Static documents in a dynamic development world

Velocity mismatch

You spend Tuesday through Thursday writing a detailed PRD. Sprint starts Tuesday the following week. By Wednesday, your senior engineer has used Claude Code to ship 40% of the feature — built to their interpretation, not section 3.2 of yours.

Two more engineers build against the wrong spec before you catch it. Rewrites. Scope debates. The PRD didn't fail because it was poorly written. It failed because it was static in a system that moves in real-time.

The compounding effect

Every static PRD that diverges from implementation adds to the debt. For mature products, that gap between what the doc says and what the product does is measured in years.

You know the product deeply — from a user perspective. But the codebase has 847,000 lines of code, 11 microservices, a legacy auth system ported from a 2018 monolith, and six years of accumulated business logic.

When you write acceptance criteria, you're writing against your mental model — not the actual, current, technical reality. Every estimate, every "done" definition, every AC is based on what you think is in the codebase. Not what's actually there.

The 90% problem

Everything above the waterline — Confluence, Jira, your PRD — is maybe 10% of what exists. The other 90% is real API behavior, undocumented edge cases, and 847 commits of micro-decisions nobody wrote down. You're making decisions about 100% of the product with 10% of the information.

02

You're Managing a Codebase You Can't Fully See

The technical opacity of mature products in 2026

Technical opacity
03

Grooming Sessions Estimating for a World That No Longer Exists

Story points built for manual code; velocity built with AI

Process breakdown

Engineers vote on story points while privately thinking "I'll use Claude Code — two hours, three points." Your acceptance criteria were written for a human building step by step. The AI agent is going to interpret the feature holistically and make a dozen design decisions you weren't consulted on.

Your ACs were written for a 2020 workflow. The implementation is happening in 2026.

What you wrote AC for

A human building sequentially over 3 days with natural clarification checkpoints

What actually happened

An AI agent that made 12 micro-decisions you were never consulted on

Goes deeper

There are 6 of these problems. We wrote up all of them.

"Done" is a lie. Discovery never makes it into code. New PMs fly blind. Read the full breakdown.

Read the full article
📄The PRD Must Evolve

The PRD as you know it
is already dead.

The PRD was designed for a world where requirements were written before code existed. Engineers implemented exactly what was specced. That contract made sense.

In 2026, Claude Code prototypes a feature in hours. The implementation happens first. The PRD gets written retroactively — if at all. By Day 3 of any sprint, your spec is already out of date.

The world needs a new kind of PRD. One that's connected to the living product. One that updates automatically as the codebase evolves. That's what AutoDoc builds.

Wrong by Day 3 of every sprint
No connection to what the code actually does
Stakeholders treat a stale doc as source of truth
Updates require PM time during the most critical phase

PRD Accuracy Over a 2-Week Sprint

How a static PRD decays vs. an AutoDoc dynamic PRD

Static PRD
AutoDoc PRD
100%75%50%25%0%Day 0Day 3Day 7Day 10Day 141st PRScope shiftRefactorAlways 100%

Every commit your team pushes keeps the AutoDoc PRD accurate

The Solution

AutoDoc DocGenerator was built
for exactly this moment.

It connects to your entire development ecosystem and does one thing no other tool provides: gives you a real-time, PM-intelligible understanding of your actual product — directly from the codebase, automatically kept current with every commit.

Connect in. Get out.

Your tools connect in
GitHub
Code, commits, PRs
Jira
Tickets, epics, stories
Confluence
Pages, decisions, docs
Linear
Issues, roadmap, cycles
Notion
Notes, specs, knowledge
AD
AutoDoc
DocGenerator
Reads your full codebase
Synthesizes all sources
Translates into PM language
Updates in real time
Always in sync
PM gets out
📝
Dynamic PRDs
Always current, code-connected
Grounded ACs
Based on what actually exists
📋
Grooming briefs
Context before every meeting
🗂
Feature inventory
What's built, right now
💬
Codebase Q&A
Ask anything, in plain language

What it actually looks like in your workflow.

Three PM workflows, completely transformed. Not generic AI features — specific tools built for how principal and VP-level PMs actually work.

01 — Dynamic PRDs

PRDs that evolve with your product. Automatically.

Connect AutoDoc to your GitHub and Jira. When you create a new PRD, it's generated from your actual codebase context — not from a blank document. When engineers ship changes, your PRD reflects them within minutes. No manual updates. No stale specs.

  • Generated from codebase reality, not imagination
  • Updates automatically on every merged PR
  • Links directly to Jira tickets and GitHub commits
  • Version history so you can see how requirements evolved
  • Shareable with stakeholders in one click
  • Always-current source of truth for the entire team
AutoDoc · PRD: Bulk Export Feature
Live
Product Requirements Document
Bulk Export: CSV & PDF
Last synced 4 min ago · 3 commits✓ Current
Current Implementation State
POST /api/exports/bulk✓ active
Rate limit:100 records/request
Formats:CSV ✓PDF — not built
Auto-Generated Acceptance Criteria
User can export up to 100 records as CSV
Export respects row-level permissions
PDF export with correct formattingpending
Async export queue for 100+ recordspending
PR #847 merged — PDF rate limiter added. Acceptance criteria auto-updated.
AutoDoc · Sprint Grooming BriefPROD-2847
User Story: Bulk notification preferences
Enterprise adminsEpic: Admin Settings v2
Technical Context (from AutoDoc)
Notification system:async queue (Redis + Bull)
Current pref model:user-level only
Similar work:PROD-1943 — role-based perms
Estimated complexity:Medium-High
⚠ Things to discuss in grooming
·Org-level override vs. user-level default?
·Retroactive for existing users or new only?
·GDPR implications for preference storage?
AutoDoc suggests 5–8 story points based on comparable PROD-1943
02 — Sprint Grooming

Walk into every grooming session with technical ground truth.

Before every grooming session, AutoDoc generates a technical brief for each story: what currently exists in the codebase, what the related systems do, what was done in similar past work, and what questions the team should debate. Engineers and PMs are aligned before the meeting starts.

  • Technical context auto-pulled from GitHub + Jira
  • Related past work surfaced automatically
  • Complexity signals from codebase analysis
  • Discussion prompts for edge cases and dependencies
  • Estimate benchmarks from comparable past tickets
  • GDPR/compliance flags when relevant
03 — Codebase Q&A

Ask your product anything. Get answers in PM language.

Stop interrupting engineers to ask "what does the payment flow actually do?" Ask AutoDoc. Get a clear, PM-level explanation drawn directly from your codebase — in under 30 seconds.

  • "What does our auth system actually do?" → instant answer
  • "What are the current API rate limits?" → from code, not Confluence
  • "What happens when a payment fails?" → full error flow mapped
  • "What was built in Q3 2025?" → commit history, summarized
  • "What does Feature X actually do vs. what the PRD says?" → gap analysis
AutoDoc · Ask Your Product
Connected to codebase
PM
What happens when a user initiates a bulk export over 10,000 records?
AD
Based on export-service/src/bulk.ts:
Exports over 1,000 records are queued via a Bull async queue. User gets an email when ready — typically 2–8 min for 10k records.
Undocumented: queue worker has a 50,000 record cap — requests above this silently fail.
Sources: 3 files · 12 commits · Jira PROD-1203
PM
That 50k cap is critical — add it to my PRD as a technical constraint.
AD
✓ Added to PRD: "Bulk Export · Technical Constraints" section. PR linked for context.

Your Monday morning, before and after.

Before AutoDoc

Sprint planning week
9:00am

Open blank document. Start writing PRD from memory + old Confluence pages.

2-3 days work
10:30am

Message senior engineer: "Quick question about how the export pipeline works." Wait for response.

Interrupts eng
2:00pm

Grooming prep: read through tickets. No context on what currently exists in codebase. Hope the team fills in the gaps.

Flying blind
3:30pm

Write acceptance criteria. Realize mid-way you don't know if the feature partially exists already.

Guesswork
5:00pm

PRD is 40% done. Blocked waiting for 3 engineering responses. Sprint planning is tomorrow morning.

Blocked

With AutoDoc DocGenerator

Sprint planning week
9:00am

Open AutoDoc. Ask: "Generate a PRD draft for bulk export v2 using our current codebase." PRD ready in 90 seconds.

90 seconds
9:10am

AutoDoc flags: "Export queue has an undocumented 50k record cap." First time you've known this.

New insight
10:00am

Grooming briefs auto-generated for all 8 stories. Each includes current codebase context, related past work, and discussion points.

All 8 stories
11:30am

Write acceptance criteria using AutoDoc's technical grounding. Every AC maps to what actually exists. Zero guesswork.

Fully grounded
2:00pm

PRD complete, reviewed, shared. Engineers say: "This is the most technically accurate PRD we've seen from a PM."

Done by 2pm

From 30-minute setup to full PM intelligence.

No engineering work required. No code changes. No custom setup by IT. You can do it yourself in half a day.

1

Connect your GitHub and Jira

5 minutes

OAuth authorization — no passwords, no tokens to manage. Select the repos and projects you own. AutoDoc gets read-only access.

Works with GitHub, GitLab, Bitbucket · Jira, Linear, Shortcut · Confluence, Notion, Docs
2

AutoDoc ingests and maps your product

2-4 hours (background)

While you're in meetings, AutoDoc analyzes your entire codebase, commit history, open and closed tickets, documentation, and past PRDs.

Processes 40+ languages · Maps system architecture · Identifies features, APIs, data flows
3

Ask your product your first question

Your first 10 minutes

Open the AutoDoc chat. Ask anything: "What does our payment system actually do?" or "Where are the biggest technical gaps in our API?"

PM-language answers · Source-linked responses · Exportable to PRD sections
4

Generate your first grounded PRD or grooming brief

Sprint planning

Start a new PRD and let AutoDoc pre-populate it with technical context. Or generate grooming briefs for every story — delivered the night before.

Templates for PRDs, user stories, ACs, release notes · Shareable links · Jira integration
5

Your documentation evolves with every PR

Ongoing, automatic

From this point, AutoDoc monitors your codebase continuously. Every merged PR, every closed ticket, every architecture change is reflected in minutes.

Real-time sync · Change notifications · PRD freshness alerts · Version history

Questions from real PMs.

The questions we hear from product managers before they try AutoDoc.

I'm not technical. Is this tool actually useful for me, or do I need to understand code?

AutoDoc was specifically designed for non-technical PMs. It reads and analyzes code on your behalf and translates everything into plain language. You ask 'what does our checkout flow do?' and get a clear, PM-level explanation — not code snippets. The entire interface is built around how PMs think and work. You don't need to understand a single line of code to get full value.

Our codebase is enormous and old. Will AutoDoc work with it, or does it only handle clean, modern codebases?

AutoDoc is designed specifically for large, complex, mature codebases — that's the primary use case. Clean, greenfield projects don't need codebase intelligence tools; the team already understands the full product. It's the 6-year-old product with 800k lines of code, undocumented legacy behavior, and multiple engineering generations that needs AutoDoc most. It supports 40+ languages including legacy stacks.

We use Claude Code and Cursor heavily. Does AutoDoc work alongside those tools?

AutoDoc is the PM layer that complements agentic engineering tools — it's not a replacement for them. Your engineers keep using Claude Code and Cursor to ship fast. AutoDoc ensures the PM side of the process (PRDs, ACs, grooming, acceptance, documentation) keeps pace with that velocity by staying connected to the live codebase.

How is this different from just asking ChatGPT or Claude about my product?

Generic AI models don't have access to your actual codebase. When you ask ChatGPT 'what does our payment system do?', it answers based on general knowledge — not your specific implementation. AutoDoc is connected to your GitHub, Jira, Confluence, and more. Every answer is grounded in your real, current product. AutoDoc knows your actual rate limits, your specific API design, your real error flows, and your particular technical debt.

What does "Dynamic PRD" actually mean in practice?

A Dynamic PRD is a living document that stays connected to your codebase. When it's created, AutoDoc pre-populates it with current technical context from your GitHub and Jira — so requirements are grounded in what already exists. When engineers ship changes during a sprint, AutoDoc detects the relevant code changes and updates the PRD automatically. You're alerted when gaps open between requirements and what was built.

Will this create more documentation work for my engineering team?

Zero additional work for engineers. AutoDoc reads what already exists — code, commits, PRs, Jira tickets — and generates documentation from it. Engineers don't need to add comments, fill in templates, or update any documentation. If anything, AutoDoc reduces engineering interruptions because PMs stop asking engineers to explain what the codebase does.

We have strict security and data residency requirements. How does AutoDoc handle our code?

AutoDoc uses OAuth integrations (read-only access) — we never ask for passwords or write access. Your code is analyzed in an isolated, encrypted environment and we are built to SOC 2 Type II standards (certification expected Q2 2026). For enterprises with strict data governance needs, AutoDoc offers full self-hosted deployment on your own infrastructure — including support for local AI models — so your code, your data, and your analysis never leave your environment at any point. Nothing touches an external API. Nothing is stored outside your control. We work with your security and legal teams directly to meet your requirements.

How quickly will I actually get value?

Most PMs generate their first grounded PRD or grooming brief within 4-6 hours of initial connection. Setup takes under 30 minutes. The AI analysis runs in the background for a few hours. The first time you ask AutoDoc a technical question and get an accurate, source-cited answer in 30 seconds instead of emailing an engineer and waiting two days — that's when it clicks.

Does AutoDoc replace Confluence or Jira?

No — AutoDoc integrates with and enhances your existing tools. Your Confluence pages, Jira tickets, and PRDs stay where they are. AutoDoc reads them as context and writes generated documentation back to them. Think of it as a documentation intelligence layer that sits on top of your entire dev ecosystem.

What about SR&ED and R&D tax credit documentation?

AutoDoc also automatically generates CRA-compliant SR&ED documentation (Canada) and IRS-compliant Form 6765 documentation (US) from your GitHub and Jira — a major additional benefit for finance teams. Read our SR&ED guide and US R&D guide for details.

Your engineers are building at
the speed of thought.

Your product decisions should be grounded in the same reality they build from.