The Memory Layer: Why Your PRD Should Be Queryable

Product development used to feel like archaeology. Three months after a decision, you'd dig through Slack threads, meeting notes, and version histories. You knew you discussed it. You knew there was a good reason. But finding that reason was painful.

It was expensive. Slower validation. Delayed launches. Missed opportunities.

I've been experimenting with a different approach. Treat your PRD as a queryable artifact of the conversations that shaped it.

The goal isn't better documentation. It's compressing time to validated product from weeks to days.

Here's what I've learned.

The Archaeology Problem

Context loss costs real money.

Context switching kills productivity, creativity, and quality. When teams stay focused without switching between projects, their velocity improves. But here's what matters more: context loss kills your ability to de-risk quickly.

When you can't access why you made a decision, you can't pivot when that assumption changes. You're stuck reconstructing. In product development, where speed to validated learning is everything, that delay is expensive.

You lose context and then you either waste days reconstructing it, or you make decisions without it and build the wrong thing. Neither is good.

The Traditional PRD Trap

Most PRDs follow a pattern. You spend days writing them. Decisions happen in meetings. Clarifications live in Slack. Someone asks a question that changes the approach, but that conversation gets buried.

The PRD captures a snapshot of what you knew at one moment. The actual context (the why behind decisions) scatters across a dozen places.

Fast forward three months. A stakeholder asks, "Why did we decide to handle service line extraction this way?" You know you discussed it. But reconstructing that reason means archaeology.

Minal Mehta, Head of Product at YouTube, says product requirements should be living documents that evolve with the product's lifecycle. Products evolve. Your requirements should adapt to new insights, changing priorities, unexpected roadblocks.

The problem: most tools don't support this reality.

Queryable PRDs: How It Works

Here's the shift: treat PRDs as living artifacts of conversation.

Every meeting transcript goes in. Every Slack thread that changes your thinking gets linked. Every technical discussion becomes part of the document. You're not just capturing what you decided. You're capturing the conversation that led to the decision.

This approach comes from Architecture Decision Records (ADRs), documents that capture why decisions were made, not just what was decided. The key insight: context-free decisions become liabilities.

I'm applying this thinking beyond technical architecture to the entire product conversation. The goal: make the "why" as accessible as the "what."

The Three-Tool Stack

I use Granola to capture meeting context automatically. It transcribes your computer's audio with no meeting bots. The conversation itself becomes the artifact.

Dia is the query layer. It's an AI browser that lets you chat with your documents and browsing history. You can ask "What was the sequence of decisions on automated extraction?" and it pulls from linked meetings and conversations. This isn't search, it's more like context reconstruction.

Then Claude Code for turning requirements into working prototypes. You delegate coding tasks through natural language. Since your requirements are already documented and accessible, there's less back-and-forth during implementation.

What This Looks Like in Practice

Here's a concrete example from a recent feature we built.

The PRD has a "Meeting Notes" section. Every weekly sync with stakeholders is linked. When discussions get technical, I query Dia to surface the original business context. This helps me simplify explanations for non-technical stakeholders.

When someone asks about a decision made two months ago, I don't search through Slack. I query the PRD: "Why did we choose X approach for handling edge cases?"

Dia pulls from linked transcripts and Slack threads. It gives me the actual conversation, not just the final decision. I see that we considered Y, discussed trade-offs, and chose X because of constraints A and B.

That context lets me make better decisions about related features. I'm not constantly bothering the people in those original conversations.

What used to take hours of archaeology now takes minutes. More importantly, I can validate pivot decisions in hours instead of days. The context is instantly accessible.

When Claude Code asks "should we handle this edge case?" I can query my PRD to see if we discussed it. We've gone from "I think we decided something about this" to "here's exactly what we discussed and why" to "here's a working prototype" in hours, not weeks.

The back-and-forth that usually happens during development is front-loaded into the PRD. You already had those conversations. You just need to access them.

The Business Case: Optionality & Speed

Here's where this workflow changes the game.

Traditional product development:

• Idea → Research (2 weeks) → Document (1 week) → Align stakeholders (1 week) → Prototype (2 weeks) → Validate → Ship
• Time to validated prototype: 6+ weeks

With queryable context:

• Idea → Capture conversations (continuous) → Query for context (minutes) → Prototype with Claude Code (hours-days) → Validate → Ship
• Time to validated prototype: Days

The difference isn't just speed. It's de-risking faster.

When you can prototype and validate in days instead of weeks, you can test more ideas in the same time window. You pivot faster when assumptions prove wrong. You capture market opportunities before they close. Less engineering time wasted on the wrong features.

More shots on goal, basically. Faster response to market signals.

Cutting Off the Downside

Nassim Taleb's concept of optionality explains why this workflow creates business value.

Taleb defines optionality as "the property of asymmetric upside (preferably unlimited) with correspondingly limited downside (preferably tiny)."

His critical insight: cut the downside first, then let the upside take care of itself.

Traditional product development has it backwards. The downside is spending 6 weeks building the wrong thing (massive, certain cost). The upside is building the right thing (uncertain benefit).

This workflow inverts it. Your downside is capped at a few days of prototyping. But the upside is you can test 10 ideas instead of 1, capture market opportunities faster. Unlimited potential.

When you can test 10 ideas in the time it takes to build 1, you only need a 10% hit rate to win. That's optionality.

As Linear's Karri Saarinen writes: "In the early days, it's better to aim to move fast as possible, try things out, learn and then fix and adjust." Queryable context is what makes that speed possible.

What Actually Works (And What Doesn't)

This isn't fully automated. It requires discipline.

You have to link conversations as they happen. Retroactive linking is painful and you'll skip it. You have to update the PRD as discussions evolve. You have to use Granola in meetings and remember to attach transcripts.

The trade-off: spend more time organizing context up front, save time not losing it later. More importantly, preserve your ability to move fast.

What works: linking conversations immediately. Don't wait or you'll forget. Query, don't search. Use AI to reconstruct context, not just find keywords.

What doesn't work: trying to make the PRD perfect before starting. Or treating this as just another process to follow.

The shift isn't about the tools. It's about recognizing that PRDs are artifacts of conversations, not standalone documents.

Still Learning

I'm not claiming this is the final answer. It's an experiment that's working better than what I tried before.

Some days I forget to link a Slack thread and kick myself later. Some meetings don't get transcribed because I forgot to start Granola. The tools aren't perfect and neither is my discipline.

But the direction feels right. Treating PRDs as queryable conversations instead of static documents. Capturing context as it happens instead of trying to document it later. Using AI to query that context instead of manually searching.

The key shift: optimizing for speed to de-risked product, not completeness of documentation.

If you're frustrated by how much context gets lost between conversations and shipping, or watching competitors move faster and wondering how, this might be worth trying.

The tools are there. The workflow takes getting used to. But constantly reconstructing context or shipping without it is slower and riskier.