Vibe Coding With a Memory — A Workflow for Building Apps That Stay On-Spec

"Vibe coding" — describing what you want and letting an AI agent build it — is genuinely powerful now. The catch isn't the coding; it's the forgetting. Halfway through, the agent loses track of what you actually asked for, and you're back to re-explaining the same requirements over and over.

Here's a workflow that fixes that. You set up a project memory once, then build against it. It works whether you're a non-technical founder with a vision doc or an engineer with a formal FRD.

The idea in one line

Keep your requirements in a knowledge base your agent can search on demand — instead of cramming them into the chat and hoping they survive. We make the case for why in Give Your AI Coding Agent a Memory; this is the how.

Step 1 — Write down what you're building

You don't need a formal spec. A plain document works: what the app does, who it's for, the key features, the rules that matter ("users can't delete a paid invoice," "onboarding is three steps"). Bullet points are fine. The point is to get your intent out of your head and into one place.

Have a PDF, a Notion export, a slide deck, or even a recorded voice memo of your idea? Even better — drop it in as-is.

Step 2 — Turn it into clean, AI-ready Markdown

Upload your doc to LLMtoMD. It converts PDFs, DOCX, slides, spreadsheets, images, audio, and whole web pages into clean Markdown — the structured format models actually reason over (headings, lists, and tables intact, not a scrambled blob). Got a reference site or a competitor's docs? Convert the URL too.

This matters more than it sounds: feed an agent a messy PDF and it fumbles; feed it clean Markdown and it reads your requirements like a human would.

Step 3 — Group it into a project

In LLMtoMD, put the documents for one app into a collection — your project's knowledge base. Now "the spec," "the API reference," and "the design notes" are one queryable unit you can point an agent at by name.

Step 4 — Connect your coding tool

Connect your agent over MCP — one click for Cursor and VS Code, one command for Claude Code. It's free on every plan, so you can wire this up before spending anything. See all the tools.

Step 5 — Build against the spec

Now the workflow changes. Instead of pasting requirements into every prompt, you let the agent pull them:

Using my project documents, build the onboarding flow exactly as the spec describes.

Before you change the billing code, check my documents for the rules about paid invoices.

The agent calls search_documents / ask_documents against your knowledge base and builds on what's actually written — not a half-remembered summary from twenty messages ago. When it's unsure, it re-checks the source instead of inventing.

Step 6 — Save decisions back

As you go, you'll make calls the original doc didn't cover ("we decided to use email magic links, not passwords"). Record them back into the knowledge base with a quick note. Now that decision is part of the project memory too — and it's there in your next session, and for any teammate's agent.

Why this beats pasting the doc every time

• It stays on-spec. Every part of the build reads from one source of truth, so features stop contradicting each other.
• It survives long sessions. What lives in the knowledge base can't get pushed out of the agent's context window.
• It's cheaper. You stop re-sending the whole document on every turn — you retrieve only the slice that's relevant.
• It compounds. Decisions you save today make tomorrow's session smarter.

That's the whole trick: build the memory once, and your AI stops forgetting.

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Start your project's memory. Convert your first document free →, then connect it to your favorite AI tool.