Building Software When Requirements Live Everywhere (And Why That's Actually Fine)

I had this beautiful dream once. We'd have perfectly structured JIRA tickets with clear requirements, a proper hierarchy of initiatives and epics, success criteria defined upfront. Everything an engineer could ever want. Then I realized that dream was holding us back.

Here's what actually happens in fast-moving startups: requirements live across Google Docs, Slack channels, meeting recordings, JIRA comments, and that one Coda document someone created three weeks ago. People are busy building, iterating, and moving fast. That's exactly what we want.

I've stopped pretending the perfect ticket structure will ever exist. Instead, I've found a way to embrace the reality of how productive teams actually work.

The Reality of Fast-Moving Teams

The scope of this challenge is massive. This isn't a small inefficiency; it's a fundamental drain on productivity that affects every fast-moving team.

In every startup I work with, requirements follow the same productive pattern:

• Initial JIRA tickets (evolving as we learn more)
• Multiple documents across Google Docs, Notion, or Coda
• Active Slack channels with real-time decisions
• Meeting recordings capturing important discussions
• Architecture diagrams living in Google Docs because commenting is easier there

This isn't broken. It's what happens when teams are building fast and iterating quickly. The challenge often stems from fundamental communication frameworks that aren't designed for the pace of modern development. People are producing content, making decisions, and moving forward. The challenge isn't the volume of content; it's that we're drowning in our own productivity.

The traditional solution? Have someone (usually the overworked product manager) consolidate everything into a single source of truth. This bottleneck often results from poor strategic time allocation where leaders spend time on information consolidation instead of higher-value activities. But this creates a bottleneck where that person becomes a single point of failure for the entire team's understanding.

Instead of trying to slow down content creation, we need to give people tools to efficiently consume what's already being produced.

The Solution: Tools for Self-Sufficient Teams

Instead of fighting the natural pace of productive teams, I've learned to embrace it. Here's the approach that's transformed how my teams consume the wealth of content they're already creating:

Step 1: Gather Everything (Don't Try to Organize It)

Collect all your requirements content:

• JIRA tickets (export as PDF)
• Design documents (download as PDFs)
• Meeting transcripts (all of them)
• Slack conversations (copy the relevant threads)
• Architecture diagrams and mockups

Understanding your team's knowledge preservation patterns can reveal which conversations contain critical decisions versus casual discussion. The key insight: don't waste time organizing this content. Just timestamp everything in the filename so AI knows what's newer. We're not creating more content. We're making existing content more consumable.

Step 2: Feed It All Into Project Knowledge

Use any of these tools:

• Google NotebookLM (excellent for references)
• Claude Projects
• ChatGPT Projects
• MCP servers for direct JIRA/Slack integration (more advanced)

Upload everything. The tool will index it all and make it searchable through natural language.

Step 3: Ask the Questions Everyone's Been Asking

Now anyone on the team can ask:

• "Generate a complete PRD based on the latest decisions"
• "What happens when a user clicks 'submit' while offline?"
• "What are the API endpoints we need to build?"
• "Create a QA test plan for the edge cases"
• "What's the backend architecture for this feature?"

The critical part: always ask for references to source material. NotebookLM is particularly good at this. It'll tell you exactly which document and section drove each answer.

Step 4: Validate and Fill Gaps

When the AI references specific decisions, you can:

• Trace back to who made that decision
• Verify the context around it
• Identify when the AI is filling gaps (potential hallucinations)
• Flag areas where actual human discussion is still needed

This last point is crucial. When AI suggests a solution without source material, it might be solving a genuine gap in your requirements. But you need to validate it with the team.

The Immediate Benefits

I've implemented this approach across multiple client teams, and the results are consistent. The impact aligns with broader research on knowledge management effectiveness:

No More Single Points of Failure: Product managers and tech leads stop being bottlenecks. If someone documented a decision anywhere, team members can find the answer without interrupting anyone's flow.

Efficient Content Consumption: Teams are producing massive amounts of valuable content. This approach helps people efficiently consume what already exists rather than recreating it or bothering colleagues.

Self-Service Problem Solving: Especially valuable for distributed teams across time zones. By implementing effective distributed team strategies, teams can reduce dependency on real-time availability. Research shows that 80% of agents say better access to other departments' data would improve their work, while 47% of professionals spend 1-5 hours a day searching for specific information. This approach addresses both issues by making information immediately accessible.

Productive Content Creation: When you can see exactly what gaps exist in understanding, content creators (PMs, tech leads, architects) can use strategic prioritization frameworks to produce targeted content that fills real holes rather than guessing what people need.

Fast Onboarding: This complements structured onboarding approaches by making institutional knowledge immediately accessible to new team members. Research by Brandon Hall Group indicates that organizations with structured onboarding programs help employees reach full proficiency 34% faster than those with shorter, administrative onboarding processes. Instead of overwhelming new team members with hours of content to consume linearly, they can ask specific questions about the parts they're actually working on right now.

Implementation Tips That Matter

Timestamp Your Content: Name files with dates so AI knows "Meeting_2024_01_15" is newer than "Requirements_2024_01_10". This prevents outdated decisions from overriding current ones.

Demand References: Always ask "What source material supports this?" If there's no reference, you've found a requirements gap that needs human attention.

Target Your Content: If you're producing requirements (as a PM) or architecture docs (as a tech lead), you can now see exactly what gaps exist and create focused content to fill them. No more guessing what people need.

Don't Create More Content: The AI gives you a massive head start on PRDs, test plans, and architecture docs, but humans should still review and approve the final output. We're consuming better, not producing more.

Start This Week

Pick your current project with the most active development. The one where everyone's moving fast and producing lots of decisions and content. Grab all that valuable content and dump it into NotebookLM or a Claude project.

Then ask it to generate a single PRD based on everything. You'll immediately see:

• What decisions are actually made
• Where the real gaps are
• Which conversations are worth having versus which answers already exist

The goal isn't to slow down or eliminate the natural pace of productive teams. It's to give everyone tools to efficiently consume the wealth of content that fast-moving teams naturally create. As your team grows, these team scaling strategies become even more critical for maintaining efficiency.

Try it with your next project. Your team will stay productive, and nobody will drown in their own success.

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