1 min lesson
The technical / product-fluency bar
Connect "You cannot coach a platform team on adoption if you only know the product from a deck" to the decision you would make.
Step 1 of 4
This stage exists to check that you can earn a senior engineer's trust. You cannot coach a platform team on adoption if you only know the product from a deck. Expect to demonstrate, not describe.
Learn more
Advanced table
Reference table
- Feature
- Tab
- What it does
- Multi-line, context-aware autocomplete that predicts your next edit.
- Why it matters in a rollout
- Fastest path to daily-active habit; the feature that hooks skeptics first.
- Feature
- Agent
- What it does
- The mode that runs multi-file, multi-step tasks across the codebase - optionally on ComposerCursor's own fast coding model, tuned for the editor and priced well below frontier models; the recommended day-to-day model for executing a plan. Press Enter for the full definition., Cursor's fast in-house execution model.
- Why it matters in a rollout
- The behavior change that drives real adoption lift and the trust conversation.
- Feature
- Chat
- What it does
- Conversational reasoning over selected code and the repo.
- Why it matters in a rollout
- Onboarding and debugging entry point for cautious devs.
- Feature
- .cursor/rules / rules & context
- What it does
- Repo-level standards and persistent context the model honors.
- Why it matters in a rollout
- How you encode an org's conventions so output fits their bar.
- Feature
- @-symbols & indexing
- What it does
- Pull specific files, docs or symbols into context.
- Why it matters in a rollout
- The difference between a generic suggestion and one that fits this codebase.
- Feature
- MCPModel Context Protocol. A standard that lets an AI agent pull in context from outside the repo, like Jira tickets or internal docs. Press Enter for the full definition.
- What it does
- Connects external tools and data sources into the workflow.
- Why it matters in a rollout
- Bridges Jira, internal docs and services into the agent's context.
| Feature | What it does | Why it matters in a rollout |
|---|---|---|
| Tab | Multi-line, context-aware autocomplete that predicts your next edit. | Fastest path to daily-active habit; the feature that hooks skeptics first. |
| Agent | The mode that runs multi-file, multi-step tasks across the codebase - optionally on ComposerCursor's own fast coding model, tuned for the editor and priced well below frontier models; the recommended day-to-day model for executing a plan. Press Enter for the full definition., Cursor's fast in-house execution model. | The behavior change that drives real adoption lift and the trust conversation. |
| Chat | Conversational reasoning over selected code and the repo. | Onboarding and debugging entry point for cautious devs. |
| .cursor/rules / rules & context | Repo-level standards and persistent context the model honors. | How you encode an org's conventions so output fits their bar. |
| @-symbols & indexing | Pull specific files, docs or symbols into context. | The difference between a generic suggestion and one that fits this codebase. |
| MCPModel Context Protocol. A standard that lets an AI agent pull in context from outside the repo, like Jira tickets or internal docs. Press Enter for the full definition. | Connects external tools and data sources into the workflow. | Bridges Jira, internal docs and services into the agent's context. |
If you cannot explain any row from your own use, you have a two-week gap to close before the loop.
Learn more
Full explanation
Whiteboard: adoption in a skeptical 500-engineer org
Whiteboard: adoption in a skeptical 500-engineer orgstructure beats coverage
Interactive diagram. Step through it with the Next and Previous controls below, or Tab to a region to read its detail.
Structure beats coverage on the whiteboard - and the instrumented pilot is the gate everything else depends on.
"Your platform team doesn't trust AI-generated code in a shared repo - that's the real objection, so let's name it. I'd pilot one willing squad, wire up telemetry from day one and set a .cursor/rules file so output matches your review bar instead of drifting. Once cycle time or review load moves on their own numbers, that squad's engineers become the ones who sell the next team."
Cursor's no-AI-in-interviews norm applies here. You won't have a tool to lean on, so the judgment has to be yours. Practice explaining a concept - say, why a rule under .cursor/rules beats prompting conventions every time - out loud, as if coaching one engineer.
Use Cursor on a real repo daily: ship one Agent task, write a rule under .cursor/rules/, wire one MCPModel Context Protocol. A standard that lets an AI agent pull in context from outside the repo, like Jira tickets or internal docs. Press Enter for the full definition. server and note where it failed. First-hand failures are the most credible thing you can bring to skeptics.
Learn more
Optional practice
Practice: The technical / product-fluency bar
QA skeptical platform team says they don't trust AI-generated code in their repo. Which first move best fits the technical-fluency bar for this role?