Support Comms, Docs & Automation
Resolve with words, then scale with tools
Writing the great support reply
After this you can respond to a frustrated technical user with clarity, empathy and accuracy.
A Technical Support Engineer at Cursor is graded on what they type. The reply is the product the user actually touches and a senior developer reads it in about four seconds to decide whether you know what you're doing.
The written exercise in the loop hands you a messy, often angry ticket and asks for a reply. Interviewers are not scoring politeness. They are scoring whether your answer resolves the issue, whether you were honest about what you don't yet know and whether a working developer would trust you with their broken editor at 11pm before a release.
Structure: acknowledge, then resolvethe shape of every good reply
Lead with one line that proves you read the ticket, then put the answer or the next step second. Burying the resolution under three paragraphs of empathy is the most common failure and on a technical audience it reads as stalling.
Interactive diagram. Tab through its regions; each focused region shows its detail in the panel below.
Four moves in order - the gate is the honesty check that keeps you from shipping a bluff.
- 1Acknowledge the specific problem. Name what broke in their words, not a generic "sorry for the trouble." "Tab stopped completing after the 1.2 update" tells them you actually read it.
- 2State the resolution or the next step. Either the fix or exactly what you're doing and when they'll hear back. This goes second, not last.
- 3Give precise, runnable instructions. Exact commands, menu paths, versions, log locations. No "try restarting" hand-waving.
- 4Set a concrete expectation. "I've reproduced this and filed it with Eng; I'll update you here by tomorrow midday" beats "we're looking into it."
Honesty beats a confident bluff
Cursor's behavior shifts as models and the product ship weekly, so you will routinely answer tickets where you don't yet know the cause. Bluffing a fix that doesn't hold is the fastest way to lose a developer's trust and they will catch it within one reply.
State the confirmed facts plainly.
"This is a known regression in 1.2 indexing on large monorepos."
Name the open question and how you're chasing it.
"I'm reproducing on a Windows + WSL setup to confirm it's not proxy-specific."
Say so, with a timeline, not a guess dressed as a fact.
"I don't have a root cause yet; I'll have an update by EOD Thursday."
Give them something to do now while the real fix lands.
"Disabling Privacy ModeCursor's setting that routes requests under zero-data-retention terms so providers don't store or train on your code. for this workspace restores indexing in the meantime."
Match the register to a developer
These users write code for a living. They want the exact version, the exact flag, the exact log line and they find padding insulting. Cut the corporate softeners and write the way a competent colleague would explain it across the desk.
- Fluffy / generic
- "Please make sure your app is up to date."
- Precise / technical
- "This is fixed in 1.2.4 - Cursor → Check for Updates or grab the build from the changelog."
- Fluffy / generic
- "Try clearing your cache and restarting."
- Precise / technical
- "Delete the index cache: quit Cursor, remove the workspace's .cursor/index folder, relaunch to force a reindex."
- Fluffy / generic
- "We apologize for any inconvenience this may have caused."
- Precise / technical
- "This one's on us - the 1.2 indexer regressed on monorepos. Here's the workaround while we ship the fix."
- Fluffy / generic
- "Our team is aware of the issue."
- Precise / technical
- "Reproduced and filed as a P1; Eng is on it. I'll post the fix version in this thread."
| Fluffy / generic | Precise / technical |
|---|---|
| "Please make sure your app is up to date." | "This is fixed in 1.2.4 - Cursor → Check for Updates or grab the build from the changelog." |
| "Try clearing your cache and restarting." | "Delete the index cache: quit Cursor, remove the workspace's .cursor/index folder, relaunch to force a reindex." |
| "We apologize for any inconvenience this may have caused." | "This one's on us - the 1.2 indexer regressed on monorepos. Here's the workaround while we ship the fix." |
| "Our team is aware of the issue." | "Reproduced and filed as a P1; Eng is on it. I'll post the fix version in this thread." |
The right column resolves and reassures in the same breath; the left column does neither.
Over-apologizing reads as weakness to a technical user. A wall of "so sorry, truly sorry, apologies again" signals you don't have a fix. Acknowledge once, own it cleanly, then demonstrate competence. Ownership and a working next step de-escalate far better than effusive sorries.
"Thanks for the detailed report - you're right that Tab went quiet after 1.2 and I can reproduce it. It's an indexing regression on large repos, not something on your end. Quick unblock: reindex by quitting Cursor, deleting .cursor/index in the workspace and relaunching. I've filed this with Eng as a P1 and I'll post the patched version in this thread. If the reindex doesn't bring Tab back, send me the output of the Cursor → Developer → Toggle Logs panel and I'll dig in."
In the written exercise, narrate your structure before you write: "I'll acknowledge the specific failure, give the workaround up top, then set a timeline." Then deliver it. Reviewers love seeing the candidate who treats a support reply as a designed artifact rather than a vibe.
Takeaway. Acknowledge the specific failure, then put the resolution or next step second - never bury it. Be exact, be honest about what you don't know and let competence do the de-escalating instead of apologies.
Self-check
QWhich reply best fits a frustrated senior developer who reports that indexing is broken?
Bug reports engineering will love
After this you can convert a user report into a report Eng can act on immediately.
A great bug report is one an engineer can reproduce without ever messaging you back. In a flat, fast org, every round-trip you save is a round-trip Eng spends fixing instead of clarifying.
User reports arrive as symptoms and emotion: "the agent is broken," "it deleted my code." Your craft is the translation layer that turns that into a precise, reproducible artifact with enough impact data that triage is obvious. This is the skill the written/async round probes when it hands you a messy report and asks for a doc or filing.
Anatomy of a filing Eng will act onthe six required parts
- Title
- Symptom + surface + scope: "Agent edits silently dropped on multi-root workspaces (1.2.3, macOS)"
- Environment
- Cursor version, OS + version, model, relevant settings (Privacy ModeCursor's setting that routes requests under zero-data-retention terms so providers don't store or train on your code., proxy), repo shape
- Steps to reproduce
- Numbered, deterministic, starting from a known clean state
- Expected vs actual
- Two short lines: what should happen, what does happen
- Evidence
- Logs, stack trace, HAR for network issues, a short screen recording - not a single screenshot of the whole screen
- Severity & impact
- Who's hit, how many, how often, is there a workaround, is data at risk
Miss any row and an engineer has to come back to you, which is exactly the cost a good report eliminates.
Minimize the repro before you file
A 40-step reproduction with the user's whole monorepo is noise. Strip it to the smallest reliable case: the fewest steps, a tiny sample repo, one model, defaults everywhere you can. A clean three-step repro tells the engineer where to put the breakpoint.
Title: Agent "Apply" silently no-ops on files with CRLF line endings (1.2.3, Windows)
Env:
Cursor 1.2.3 (build 24f1a)
Windows 11 23H2
Model: default agent model
Privacy Mode: ON
Repo: fresh, single file, no .cursorignore
Repro (from clean state):
1. New folder, add main.py saved with CRLF line endings
2. Ask agent: "add a docstring to the top function"
3. Agent shows a diff and clicks Apply
Expected: docstring written to main.py
Actual: Apply reports success, file is unchanged on disk
Logs: see attached cursor-logs.txt (search "apply: 0 hunks written")
Impact: P1 for Windows users on CRLF repos - silent data-loss-shaped
bug (user believes edit landed). ~6 tickets this week, no workaround.Quantify impact and link the pattern
Triage is a prioritization problem, so make the priority self-evident. Put numbers on breadth and frequency, then link the related tickets. Five separate "agent didn't apply" reports look like noise; one filing that links all five turns them into a pattern and a pattern is roadmap signal.
- Breadth: how many users or accounts and whether any are enterprise - a single large customer changes the calculus.
- Frequency: every time vs. intermittent. Note your repro success rate, e.g. "4 of 5 attempts."
- Severity shape: cosmetic, blocking or data-at-risk. A silent edit-loss outranks a slow autocomplete even at lower volume.
- Pattern links: cross-reference sibling tickets so Eng sees scope, not a one-off.
Escalate by handing over the chat, not a dry report
Some tickets still need engineering - genuine platform bugs, or code-specific knowledge a generalist can't resolve as accurately as someone in that code daily. The stronger handoff starts with ask mode: use it to investigate, triage and qualify (is this a bug, can we fix it quickly, can we educate better), gather your own context to minimize thrash, then escalate by sharing the actual Cursor chat. Engineering sees where you got to and what the end state was - real context, not a dry "this looks like a bug." In a fast-growing org the line blurs further: support also fixes bugs and ships small features themselves with Cursor before anything reaches the Eng queue.
Share the chat itself so the engineer inherits your investigation instead of starting cold.
"Share the Cursor chat there directly so they can have context on where they've reached, what the end state was... instead of just it being like a dry context and like this looks like a bug type of escalation."
Before you file, reread it as the engineer who's never seen the ticket. Can they reproduce from a clean machine using only what's written, without messaging you? If anything forces a follow-up question, that's the gap to close before filing.
If the async exercise gives you a rambling user report, write the bug report and say what you did: "I stripped this to a three-step repro, pinned the version and OS and quantified impact so triage is obvious." Showing the minimization step is what separates a support person from a support engineer.
Takeaway. File reports an engineer can reproduce without pinging you: minimized repro, pinned environment, expected-vs-actual, evidence and quantified impact. Link sibling tickets so a cluster reads as a pattern, not noise.
Self-check
QWhy minimize a reproduction before filing a bug, instead of just forwarding the user's full description?
Docs & knowledge bases
After this you can write self-serve docs and runbooks that deflect the next ten tickets.
Maintaining and expanding user-facing docs and internal knowledge bases is a named responsibility in this role, not a side task. The best support engineers answer a question once in a ticket, then write it down so they never answer it again.
Every recurring ticket is a doc that doesn't exist yet. A KB article that deflects ten future tickets is worth more impact than ten perfect individual replies and at Cursor's pace that impact is how a small support team keeps up with a product that ships weekly.
Anatomy of a KB article that actually deflectsstructure users can self-serve from
- 1Symptom-first title. Users search the error they see, not the cause. "Tab autocomplete stopped working" gets found; "Resolving index cache invalidation" does not.
- 2Prerequisites up front. Version, OS, plan or settings the steps assume, so a user on the wrong setup bails early instead of midway.
- 3Numbered steps with exact actions. Menu paths, commands, file locations. Each step does one verifiable thing.
- 4A 'still stuck?' escalation path. Where to go and what to collect (logs, version) if the steps don't fix it - so the resulting ticket arrives complete.
Find the doc gaps in your ticket queue
You don't guess what to document. The queue tells you. Cluster recent tickets by symptom and the tallest cluster without a corresponding article is your next doc.
- Ticket cluster signal
- Same question 8+ times this month
- What it tells you to write
- A symptom-first KB article - clearly nothing self-serve exists yet.
- Ticket cluster signal
- Users find a doc but still file the ticket
- What it tells you to write
- The doc exists but is wrong, stale or buried - fix or surface it, don't add a second one.
- Ticket cluster signal
- Spike right after a release
- What it tells you to write
- A migration / changelog note: the product changed and the docs didn't keep up.
- Ticket cluster signal
- Team keeps re-deriving the same triage steps
- What it tells you to write
- An internal runbook, so any teammate resolves it the same way without re-thinking.
| Ticket cluster signal | What it tells you to write |
|---|---|
| Same question 8+ times this month | A symptom-first KB article - clearly nothing self-serve exists yet. |
| Users find a doc but still file the ticket | The doc exists but is wrong, stale or buried - fix or surface it, don't add a second one. |
| Spike right after a release | A migration / changelog note: the product changed and the docs didn't keep up. |
| Team keeps re-deriving the same triage steps | An internal runbook, so any teammate resolves it the same way without re-thinking. |
The queue is a backlog of documentation, ranked by how often each gap costs you a ticket.
Internal runbooks vs. user docs
User docs help a customer self-serve. Runbooks help your team resolve consistently. Both matter and they're written for different readers.
Audience: the customer in the moment of pain.
Goal: deflect the ticket entirely.
Voice: plain, symptom-first, no internal jargon.
Audience: a teammate triaging the ticket.
Goal: same diagnosis and fix every time, regardless of who's on shift.
Voice: precise, links to logs, escalation owners, known caveats.
Support owns the docs - no over-the-fence handoff
The old loop was slow: support spots a doc gap, logs a ticket, and a technical writer eventually picks it up. With the docs open in their own Cursor workspace, a support engineer can check whether something is documented, make the change, render the doc site to review it and open a PR - all off the same customer interaction. Updating docs is low-risk, high-agency, high-impact work, and it builds a tight feedback loop where the docs improve continuously based on what support actually hears from customers.
Keep the docs reopened in your Cursor instance and the writer handoff disappears - you ship the fix from the same interaction that surfaced it.
"It removes that sort of like fence in the middle... they can go and update the docs pretty much directly based off of a Cursor interaction."
Guard knowledge-base quality on the intake side
AI-generated content can quietly rot a knowledge base with slop - ancillary comments that explain the model's thinking but add nothing long term. Cursor keeps a team command called de-slop for exactly this: stripping the AI artifacts they don't want. For a KB, the instruction is to be verbose only where it earns its place and concise everywhere else. The preferred fix is a guardrail at intake, when someone submits a change, rather than auditing already-published content after the fact.
A standing team command removes the AI cruft, and the quality gate sits where changes enter - not as a post-hoc review of what's already live. A background agent can sweep the KB for improvements, but intake guardrails do more to keep it clean.
"We have a team command called de-slop which is our term for like getting rid of AI things that we don't necessarily want."
Because Cursor changes weekly, a doc that was right last month can be the thing that confuses a user today. A wrong doc is worse than no doc, because the user follows it, fails and files a more frustrated ticket. Treat doc freshness as part of every release: when a feature changes, the article changes with it.
When asked how you'd reduce ticket volume, don't say "write more docs." Say how you'd find what to write: "I'd cluster the last month of tickets, find the tallest cluster with no good article and write a symptom-first KB with an escalation path - then watch whether that cluster shrinks." That closes the loop and shows you measure deflection, not output.
Takeaway. Recurring tickets are missing docs. Mine the queue for the tallest cluster, write a symptom-first article with prerequisites, numbered steps and a 'still stuck?' path - and keep it fresh, because a stale doc generates angrier tickets than no doc.
Self-check
QWhat's the most reliable way to decide which knowledge-base article to write next?
Automations that scale support
After this you can design tooling that removes toil, the way Cursor expects a TSE to.
This is an engineering-flavored support role and building the automations that scale support operations is a core, named responsibility. The tooling round checks whether you instinctively reach for a script when you see the same task done by hand twice.
The mindset Cursor screens for is simple: toil is a bug. If you find yourself copy-pasting the same triage steps, hunting the same log line or retyping the same macro, that's a candidate for automation. The interview won't ask for production code; it asks how you'd design the thing and why it's worth building.
high-impact automation targetswhere a script pays for itself fast
Interactive diagram. Tab through its regions; each focused region shows its detail in the panel below.
Build top-left first: low effort, high impact. Plot any tooling idea here before you write a line.
Classify and route incoming tickets by surface (Tab, agent, billing, SSOSingle Sign-On. One company login (usually via SAML or OIDC) instead of a separate password per tool.) and severity.
impact: the right human sees it first, faster - no manual sorting.
Pull the signal lines out of a pasted Cursor log dump automatically.
impact: skip the manual scan, surface the error and version instantly.
Spin up a clean, known-state workspace to reproduce a reported bug.
impact: consistent, fast reproduction instead of hand-built setups.
Draft a first-pass reply or KB stub a human edits and sends.
impact: speed and consistency, with a person still owning the send.
The toolkit you'd actually reach for
A bot that reads new tickets, pulls the version and error from the log paste, classifies the surface and posts a routed summary into the right Slack channel is a weekend project and an outsized win. Sketch it at the level of inputs, transform and output.
# Triage bot: new ticket -> classify -> route to Slack
on new_ticket(t):
log = extract_log_block(t.body) # pull pasted Cursor logs
info = parse(log) # version, OS, first error line
area = classify(t.subject, info.error) # tab | agent | billing | sso | other
sev = severity(info.error, t.keywords) # p0..p3 heuristic
summary = llm_draft_summary(t, info, area) # human-editable, not auto-sent
post_to_slack(channel=route(area), text=summary, sev=sev)
tag_ticket(t, area=area, severity=sev)
# Win: every ticket lands pre-classified with version + error surfaced,
# so the on-call engineer reads signal instead of digging for it.Always frame automation by impact
An engineer at Cursor doesn't care that you can write a script. They care what it bought. Frame every automation in the language of impact: time saved, tickets deflected or consistency gained.
- Log parser
- "Cut average triage time per ticket from ~6 min to under 1."
- Auto-triage routing
- "Right engineer sees P1s first; mis-routes dropped to near zero."
- Smart macro
- "Consistent, correct repro steps in every reply, no copy-paste drift."
- KB self-serve
- "That cluster's ticket volume fell by half the month after."
Impact, not implementation, is what the tooling round and the resume bullet are really asking for.
How Cursor's own support team runs on Cursorthe workflow the workshop describes
Inside Cursor, a support engineer works less like a single chat operator and more like an orchestrator. The Agents window lets you spin up many agents at once - one per incoming ticket - then review the work as it streams in and tap into each individually with little context switching. "How do I log in with SCIMSystem for Cross-domain Identity Management. A standard for automatically creating and removing user accounts when people join or leave." runs in one agent while "what does the analytics dashboard show" runs in another, so you keep a steady flow of ticket volume instead of serializing on a single thread.
Time is the scarcest thing a support team owns, so the orchestrator pattern - many agents in flight, reviewed as they finish - is how one person keeps up with volume.
"The biggest and most important currency that we have at Cursor and support team is time."
Bring the context in, not just the code
Looking at the code helps, but the real unlock for a support team is pulling the platforms you lean on most into Cursor over MCPModel Context Protocol. A standard that lets an AI agent pull in context from outside the repo, like Jira tickets or internal docs.. Three pillars cover almost everything: ticketing, a knowledge base and customer context. Cursor's own stack wires up Datadog to pull logs straight in for debugging, Notion for the docs they live in and Linear for bug reports.
Interactive diagram. Tab through its regions; each focused region shows its detail in the panel below.
Code alone is one layer; the extra context sources are what make support seamless. Cursor's own picks in parentheses.
The platforms the team relies on - ticketing, KB, CRM, logs - are what turn isolated answers into joined-up support.
"If you're just looking at the code that can be helpful in and of itself but providing these additional sources of context is what really unlocks a team."
Meet support where it happens: Slack
Cursor's Slack integration lets you invoke Cursor from where support conversations actually live. Ask a question in a thread - "is there dark mode support in the app" - and Cursor launches a cloud agent that answers. Follow up with "Cursor, implement a dark mode" and it spins up a remote agent on a fresh VM and takes a shot at the feature. The Slack conversation is mirrored to cursor.com, where you can follow the diff line by line.
A team variant turns this into async, shared support: a dedicated channel where people spin up background agents and ask questions in the open. The rest of the team sees the agent conversation and can engage - if they know the answer they jump in, if they don't they learn. You get asynchrony (no need to stay glued to the agent in the IDE) plus a layer where knowledge gaps surface and get shared.
Run agents in a shared Slack channel, not just your local IDE - the conversation becomes a team artifact others can learn from.
"Having like a shared space, whether it be in Slack or elsewhere to engage background agents, ask questions, have these asynchronous conversations."
Scripting to bridge data the dashboard doesn't expose
Customers ask for bespoke cuts of data the product doesn't surface - say per-user clicks per day when only combined clicks exist. A support engineer knows the platform well enough to scope this themselves: first check whether an existing API can provide it, and if not, write a small script (sometimes pulling a third-party API) to gather the info, one-off or recurring. The judgment is in keeping it small. Don't write a whole new API, don't stand up a new service on a cloud provider - if the ask really needs that, it's a feature request, not a support script.
You're babysitting the agent toward a bounded result, not letting it build infrastructure. "I don't want you to necessarily write a whole API... don't necessarily add a new service. Don't host something on a cloud provider. Don't go crazy." If it outgrows a script, qualify it as a feature request instead.
Auto-run: agents that triage high-confidence tickets
The direction this is heading is autonomous triage. Cursor uses an auto-run workflow internally that fires a background agent on incoming bug reports that meet a confidence bar. The agent runs on its own, then a human reviews: merge if it's a high-confidence fix, or iterate with the agent if not. It's currently shaped around Linear's schema, with more connectors coming. The biggest lesson wasn't about the agent at all.
The background agent was effective with the data it had; the work was fixing the intake process so the right data existed in the first place.
"We need to iterate on our bug reporting process a little bit to make the right data available."
Walk into the tooling round with a real "I automated X and it saved Y" story, told start to finish: the toil you noticed, what you built, the language and APIs you used and the measured result. One specific, quantified story beats any amount of "I'm comfortable scripting."
LLM-assisted replies are impact, not autopilot. For an AI dev-tools company supporting senior engineers, an auto-sent wrong answer is a credibility hit. Draft with the model; have a human verify and own the message. Say this out loud in the round - it shows judgment, not just enthusiasm for automation.
Takeaway. Treat repeated toil as a bug worth scripting: triage bots, log parsers, repro scripts, LLM-assisted drafts. Always justify the tool by impact - time saved, tickets deflected, consistency gained - and keep a human on the send.
Self-check
QHow should you frame an automation you built when describing it in an interview?
Representing Anysphere externally
After this you can hold a credible technical conversation with senior enterprise developers.
When you talk to an enterprise customer, you are Anysphere to them. In a technical conversation with a senior developer, your credibility is the product - they decide whether to trust Cursor partly on whether they trust you.
The cross-functional round probes this directly: can you hold your own with people who build software for a living, speak their constraints and stay honest under pressure. The candidates who pass aren't the ones with the smoothest answers. They're the ones who are precise, admit limits cleanly and follow up fast.
Interactive diagram. Tab through its regions; each focused region shows its detail in the panel below.
The cross-functional and values rounds weigh these in roughly this order - lead with the top ones.
Speak the customer's stack and constraints
Enterprise developers live inside constraints you have to speak fluently: security, compliance, scale and how a tool fits their existing pipeline. Generic enthusiasm bounces off them; specificity earns the room.
- Security & data handling
- Privacy ModeCursor's setting that routes requests under zero-data-retention terms so providers don't store or train on your code., what's sent to models, what's retained, where code lives
- Identity
- SSOSingle Sign-On. One company login (usually via SAML or OIDC) instead of a separate password per tool. / SAMLAn enterprise standard that powers single sign-on., admin and team management, provisioning
- Deployment & rollout
- How it lands across hundreds of devs, network/proxy, version control
- Scale
- Large monorepos, indexing behavior, performance at their repo size
- Fit
- How Cursor sits next to their CI, review tooling and existing IDE setup
Naming the concern in their vocabulary signals you understand their world before you've pitched a thing.
Truth-seeking over face-saving
The instinct to never say "I don't know" in front of a customer is exactly the instinct Cursor is screening against. Anysphere prizes truth-seeking and a guessed answer that turns out wrong costs more trust than a clean "I'll confirm and follow up" ever could.
- Tempting (face-saving)
- Confidently guess how data retention works.
- Credible (truth-seeking)
- "I don't want to guess on data handling - let me confirm the exact retention behavior and follow up today."
- Tempting (face-saving)
- Promise a feature is "coming soon" to close the moment.
- Credible (truth-seeking)
- "That's not something I can commit to a date on. Here's what exists today and I'll flag the request to Product."
- Tempting (face-saving)
- Wave away a real limitation.
- Credible (truth-seeking)
- "You're right, that's a current limitation. Here's the workaround and I'll make sure Eng hears the impact."
| Tempting (face-saving) | Credible (truth-seeking) |
|---|---|
| Confidently guess how data retention works. | "I don't want to guess on data handling - let me confirm the exact retention behavior and follow up today." |
| Promise a feature is "coming soon" to close the moment. | "That's not something I can commit to a date on. Here's what exists today and I'll flag the request to Product." |
| Wave away a real limitation. | "You're right, that's a current limitation. Here's the workaround and I'll make sure Eng hears the impact." |
The right column loses the moment but wins the relationship - and is the answer Cursor's values round rewards.
Close the loop back to Eng and Product
A hard external conversation is only half done when the call ends. The other half is carrying that customer's signal back into the roadmap loop, which is an explicit part of the role: you partner with Product and Engineering to feed customer insight into roadmap decisions.
- 1Capture the real signal. Not "customer wants X," but the underlying constraint: "a 2,000-dev org can't roll out without SCIMSystem for Cross-domain Identity Management. A standard for automatically creating and removing user accounts when people join or leave. provisioning."
- 2Quantify and attribute. Which account, how big, how blocking - so Product can weigh it honestly against other work.
- 3Route it where it lands. File it, link related tickets and tag the right Eng/Product owner rather than letting it die in your notes.
- 4Follow up with the customer. Close your own loop: tell them what you escalated and what happened, even if the answer is "not yet."
"On data handling, I don't want to give you a half-right answer - let me confirm exactly what's retained under Privacy ModeCursor's setting that routes requests under zero-data-retention terms so providers don't store or train on your code. and send it over by end of day. On the monorepo indexing you flagged, that's a real constraint at your repo size today; here's the current behavior and the workaround and I'm going to bring your numbers back to our team because that's exactly the kind of signal that moves the roadmap."
When a cross-functional interviewer pressures you with a question you genuinely don't know, resist the bluff. Say "I'd confirm rather than guess and here's how I'd find out fast," then describe where you'd check and when you'd follow up. Demonstrating truth-seeking under pressure is worth more than a fluent wrong answer.
Takeaway. To a customer you are Anysphere, so credibility is the product: speak their security and scale constraints natively, confirm rather than guess and always carry the customer's quantified signal back into the Eng and Product loop.
Self-check
QA senior enterprise developer asks a pointed question about Cursor's data retention that you can't answer with certainty. What do you do and why does it matter to Anysphere?