How to Make Cursor Build What Users Actually Want

You open Cursor, type a prompt, and watch it generate a feature in minutes. It's magic. The code is clean, the tests pass, the PR looks great.

Then you ship it and nobody uses it.

This isn't a Cursor problem. It's not a Copilot problem or a Claude Code problem. It's a context problem. Your coding agent is building exactly what you told it to build. The issue is that what you told it has almost nothing to do with what your users actually need.

The disconnect between prompts and user needs

When a developer prompts a coding agent, the input chain typically looks like this:

1. A user has a problem
2. They complain — in a support ticket, an NPS survey, a tweet, a Slack message
3. A PM hears about it (maybe), interprets it (loosely), and writes a ticket (eventually)
4. A developer reads the ticket, interprets it again, and writes a prompt
5. The coding agent reads the prompt and builds something

By step 5, the original user signal has been through four layers of interpretation. It's a game of telephone where the last player is an AI that takes instructions very literally and executes at superhuman speed.

The result? Features that are technically impressive and totally irrelevant. A sorting algorithm when users wanted a search bar. A settings page when users wanted a default change. A reporting suite when users wanted one number on the home screen.

What your coding agent actually sees

Let's be honest about what Cursor, Copilot, and Claude Code have access to when they're building your product:

Your agent is building with half the picture. The code half. The user half — the half that determines whether anything you build actually matters — is completely invisible to it.

Bridging the gap: from user signal to agent context

The fix is straightforward in theory: get user feedback into the place where your coding agent can read it. In practice, this is where most teams fall apart — because feedback is scattered across a dozen tools and nobody has time to synthesize it.

This is what Pulse does. It connects to your existing feedback sources — Intercom, Amplitude, Hotjar, Canny, NPS tools — analyzes the signals using AI, and generates a structured context file called PULSE.md that lives in your repo. Your coding agent reads it automatically.

Here's what the workflow looks like:

Step 1: Connect your feedback sources

Pulse pulls from the tools you already use. Intercom conversations, support tickets, Canny feature requests, NPS survey responses, Amplitude event data. No new tools for your team to adopt — just a layer that reads what's already there.

Step 2: Pulse analyzes and prioritizes

Raw feedback is noisy. Pulse uses AI to cluster related signals, identify patterns, and rank issues by impact. Fifty users saying the same thing in different words? That becomes one clear insight with a severity score and supporting evidence.

Step 3: PULSE.md lands in your repo

The output is a markdown file — PULSE.md — that gets committed to your repository. It's structured so both humans and AI agents can parse it instantly. Here's a simplified example:

# PULSE.md — User Feedback Context
## Last updated: 2026-02-14

### 🔴 Critical (High volume, high severity)
- **"Can't find my revenue numbers"** — 89 Canny votes, 47 Intercom
  mentions this month. Users navigate Settings → Billing → Usage
  to see basic revenue. Average: 2.3 visits/session to this flow.
  → Recommendation: Surface revenue on main dashboard.

### 🟡 Moderate
- **"Email notifications too frequent"** — 34 mentions, NPS detractor
  theme. Users want digest mode, not per-event emails.
  → Recommendation: Add notification frequency settings.

### 🟢 Low
- **"Dark mode on mobile"** — 12 Canny votes, nice-to-have.
  Not correlated with churn.

Step 4: Your coding agent reads PULSE.md

When you open Cursor and start a task, PULSE.md is part of the context. The agent doesn't just know what to build — it knows why and for whom. If your prompt conflicts with what users are actually saying, the context is right there to course-correct.

Instead of "Build an analytics dashboard" → the agent sees 89 votes for "show revenue on home screen" and builds the right thing. Two days instead of three weeks. $3K instead of $50K.

Why a file in the repo?

We chose a markdown file over an API, a dashboard, or a plugin for one reason: it's where coding agents already look. Cursor reads your repo files. Claude Code reads your repo files. Every coding agent understands markdown. No integration needed — it's just a file.

It also means your entire team can see it. PMs can review priorities. Designers can check user pain points. Engineers can read the context before prompting. It becomes a shared source of truth about what users need, updated continuously from real data.

The practical difference

Without Pulse, the workflow is: PM interprets feedback → writes ticket → dev interprets ticket → writes prompt → agent builds something → hope it's right.

With Pulse: feedback sources → AI analysis → PULSE.md → agent reads real user context → builds the right thing.

You're cutting out the telephone game. The user's voice — aggregated, analyzed, and structured — goes directly into your agent's context. No more building in the dark.

The coding agents we have today are extraordinary tools. They can build almost anything in almost no time. The bottleneck was never speed. It was always knowing what to build. Fix the input, and the output takes care of itself.