Design an IDE Code Assistant

Interview problem: provide fast inline completions, explanations and developer-requested edits inside an IDE, while protecting repository data and preserving the developer's work.

An inline completion is a proposed insertion shown at the current cursor, often as ghost text. A coding task is a larger operation that may inspect files, propose a patch and run checks. These have different latency, context and execution requirements.

This is a hypothetical Learnastra interview scenario. Targets and workload assumptions are illustrative. The key design decision is separating the fast suggestion path from the slower, isolated edit-and-test path.

1. Requirements and boundaries

Clarify supported IDEs/languages, local versus hosted processing, repository size, whether unsaved buffers are available, whether tools can execute code, and what counts as an accepted change. Start with a few well-supported languages, then extend the language adapters.

Functional requirements

1. Suggest short inline completions using the current cursor, code prefix and suffix.
2. Explain selected code and diagnostics using relevant definitions and project context.
3. Generate functions, documentation and proposed refactors on explicit request.
4. Present multi-file changes as a reviewable diff tied to the original file versions.
5. Run feature-appropriate syntax, type, security and test checks, reporting failures and unavailable checks distinctly.
6. Let users accept, reject, cancel, undo and adjust suggestion frequency.
7. Support a private deployment in which code, embeddings, inference and telemetry remain within approved customer infrastructure.

Excluded from the initial product: autonomous production deployment, unrestricted secrets/network access, and an assumption that every generated patch may be committed or merged without the developer's authorization.

Nonfunctional requirements

1. Target p50 inline suggestion display below 200ms and p99 below 500ms from the triggering editor event, including debounce and rendering.
2. Target first useful output for a small requested generation within three seconds under the tested workload. Multi-file edits have a separate job deadline and progress UI.
3. Keep typing responsive: context gathering and network work must not block the editor thread.
4. Reject suggestions for stale document versions, cursors or superseded requests.
5. Enforce repository, path and tenant access on retrieval, caches, tools and results.
6. Bound input/output tokens, concurrent requests, sandbox resources and task spend.
7. Measure useful retained changes, defects, latency and cost; scans reduce known risk but cannot guarantee zero vulnerabilities.

An acceptance-rate target such as 30% is an experiment objective, not a correctness guarantee. A higher acceptance rate can result from showing only trivial completions.

2. Estimate load and isolate the two workloads

Assume 100,000 daily active developers, 200 completion requests/day each after client-side suppression, and five requested coding tasks/day each.

These are not keystroke counts. Debouncing, cancellation and “do not suggest here” rules determine how much typing becomes inference traffic. Geography and work hours determine real peaks.

At 120ms mean server residence, the completion peak implies roughly 2,315 × 0.12 ≈ 278 in-flight requests under stable assumptions. A two-minute mean task at 58 arrivals/second implies around 6,960 active jobs—an entirely different resource problem. Queue, admission and pricing policies must control this workload; do not run it on the same unconstrained pool as typing suggestions.

3. Start with a completion-only baseline

flowchart LR
    E[Editor event] --> S[Capture document version and exact cursor]
    S --> C[Fit nearby prefix and suffix]
    C --> M[Approved completion endpoint]
    M --> V[Fast checks and current-snapshot comparison]
    V --> G[Display ghost text]
    G --> A[Developer accepts or dismisses]

Fill-in-the-middle, or FIM, conditions generation on code before and after the insertion point. Codestral 2508 remains a documented specialized FIM candidate as of this review; choose a licensed, evaluated local alternative when the private boundary requires it. A model name does not guarantee 200ms delivery.

Improve only after locating the failure

4. Detailed architecture

flowchart TD
    subgraph IDE[Developer environment]
        EVT[Editor events and selected task] --> SUP[Debounce, cancel and local policy]
        SUP --> CTX[Unsaved buffers, cursor and symbol context]
        IDX[Incremental local repository index] --> CTX
        CTX --> GW[Authenticated gateway and repository scope]
        VIEW[Ghost text or diff with check results]
    end
    GW --> ROUTE{Request class}
    ROUTE -->|Inline| FAST[Short deadline and completion pool]
    FAST --> FV[Fast validation and snapshot fence]
    FV --> VIEW
    ROUTE -->|Edit or explanation| JOB[Durable job queue and budgets]
    JOB --> SNAP[Authorized repository and buffer snapshot]
    SNAP --> BOX[Disposable isolated workspace]
    BOX --> AG[Bounded model and tool workflow]
    AG --> TEST[Syntax, types, security and relevant tests]
    TEST --> DIFF[Complete diff and evidence tied to base version]
    DIFF --> VIEW
    VIEW --> APPLY[Developer acceptance and version-checked application]
    AG -. tool decisions and usage .-> TEL[Restricted telemetry]
    FAST -. timing and stale-response counts .-> TEL

VS Code exposes an inline-completion provider API; language-server features can supply symbols, definitions and diagnostics. Keep UI integration separate from retrieval/model serving so other editors can reuse the backend.

A private deployment must keep every code-bearing path private, including query embeddings, crash reports, caches, test logs and backups. An on-premises index paired with an external generator does not satisfy “no code leaves.”

5. Context assembly must match the editor snapshot

Prioritize the exact insertion context, enclosing function/type, relevant imports and definitions, then other relevant files. “Open in another tab” is a weak relevance signal by itself.

1. Capture repository/workspace identity, document URI, buffer version and cursor.
2. Split prefix/suffix at the exact character position, including unsaved changes.
3. Obtain nearby symbols using the editor/language service or a versioned index.
4. Exclude unauthorized paths and configured sensitive files before retrieval.
5. Fit instructions, FIM markers, references and code with the selected tokenizer.
6. Reserve output space and recheck the complete formatted request size.
7. Attach snapshot/request IDs so the response can be rejected if the editor changes.

A 4,000-token input experiment might allocate roughly half to nearby code and the remainder to definitions/references. The split is a tuning parameter; never drop a critical suffix or change the insertion boundary merely to fill the budget. See context engineering.

Cursor positions: bytes, UTF-16 units and code points differ

This helper uses zero-based lines and Python character offsets. Convert the editor protocol's position encoding before calling it; a non-BMP character such as an emoji occupies two UTF-16 code units but one Python character.

def split_at_cursor(content, line, column):
    lines = content.split("\n")
    if not 0 <= line < len(lines) or not 0 <= column <= len(lines[line]):
        raise ValueError("Cursor is outside the document")
    offset = sum(len(part) + 1 for part in lines[:line]) + column
    return content[:offset], content[offset:]

assert split_at_cursor("ab\ncdef", 1, 2) == ("ab\ncd", "ef")
assert split_at_cursor("a😀b", 0, 2) == ("a😀", "b")

Use the editor's native range/document helpers where possible. Normalize line-ending handling explicitly; offsets calculated from a normalized copy must not be applied to an unnormalized buffer.

API and state contract

Completion request: request_id, document_uri, buffer_version, cursor,
                    prefix, suffix, language, context_digest
Completion result:  request_id, buffer_version, replacement_range, text,
                    model_config_version, fast_check_status
Edit job result:     job_id, base_snapshot, complete_patch, check_results,
                    changed_files, status, usage

Server-side authenticated scope supplies tenant/repository authority. Request fields describe the desired work; they cannot authorize access to another repository.

6. Cancellation and patch application

Cancel obsolete requests to save work, but still check their responses. Cancellation can race with a completed remote response or fail to stop an already running generation.

sequenceDiagram
    participant E as Editor
    participant S as Suggestion service
    participant M as Model
    E->>S: Request R1 at buffer version 41
    S->>M: Generate for R1
    E->>E: User types, buffer becomes version 42
    E->>S: Cancel R1 and request R2
    M-->>S: Late R1 result
    S-->>E: R1 response carrying version 41
    E->>E: Reject stale result
    S->>M: Generate for R2
    M-->>S: R2 result
    S-->>E: Current response with replacement range
    E->>E: Revalidate snapshot and show ghost text

Before applying a multi-file patch, compare the current buffers and files with the job's base snapshot. If they differ, show a conflict or construct a reviewed merge and rerun affected checks. Never overwrite uncommitted developer changes to make a patch fit. Preserve an undo transaction and show all changes, not only the last edit of a multi-step job.

A regenerated or rebased diff is a new artifact: earlier tests and approval may no longer apply. Check results must refer to the actual proposed bytes.

7. Edit-and-test workflow and sandboxing

The slow path reads relevant code, proposes a bounded patch, validates it, and returns the full diff. An optional best-of-N strategy generates alternatives, but count every candidate's model/test cost and evaluate whether selection improves useful outcomes. Eight candidates are not a universal recommendation.

Tests, build scripts, dependency installers and plugins execute repository-controlled code. Run them in a disposable workspace with resource limits, restricted network/egress, scoped filesystem mounts and no production secrets. A container alone is not a complete hostile-code boundary; choose stronger isolation when the threat model requires it.

Canonicalize paths and handle symlinks at the enforced filesystem/tool boundary. A textual startsWith(allowedPath) check is insufficient. Retrieved comments and files are untrusted task data; they must not grant tools permission to publish or exfiltrate code. See agent security and sandboxing.

Feature-specific verification

Incomplete code at a cursor can be valid as a proposed fragment even when it is not a standalone program. Validate the insertion in context. Do not market a 200ms suggestion as having passed a full multi-language security analysis.

For edit jobs requiring syntax and security checks, the executable gate below makes missing or failed checks block a “ready for review” status:

def verification_summary(results, required_checks):
    if not {"syntax", "security"} <= set(required_checks):
        raise ValueError("Edit policy must include syntax and security checks")
    incomplete = [name for name in required_checks if results.get(name) != "passed"]
    return {"ready_for_review": not incomplete, "blocking_checks": incomplete}

required = ["syntax", "security", "types", "tests"]
assert verification_summary(dict.fromkeys(required, "passed"), required)["ready_for_review"]
assert not verification_summary({"syntax": "passed", "security": "passed"}, required)["ready_for_review"]

This gate consumes tool results; it does not certify security. A developer may inspect an incomplete diff, but the UI must distinguish it from a fully checked one.

Concrete Python syntax check

import ast

def check_python_syntax(source):
    try:
        tree = ast.parse(source)
        compile(tree, "<proposed-change>", "exec")  # Compilation, not execution.
        return {"status": "passed", "reason": None}
    except SyntaxError as exc:
        return {"status": "failed", "reason": f"Syntax error on line {exc.lineno}"}
    except (MemoryError, RecursionError):
        return {"status": "unavailable", "reason": "Parser resource limit"}

Parsing alone does not perform every compilation check; Python's AST documentation explains the distinction. Run even parsers in a size/process-limited environment. Language-specific type, security and test tools need their own adapters and error interpretation. For example, pytest exit code 5 means no tests were collected, not that the proposed behavior passed tests. Missing tooling, timeout and failing tests are different results.

8. Latency and caching tradeoffs

For an illustrative 200ms path, allocate 40ms debounce, 20ms context work, 30ms network/gateway, 90ms inference and 20ms checking/rendering. The sum is 200ms; it is not proof that a real remote model meets the target. A 150ms debounce alone would consume most of this budget. Component percentiles cannot simply be added to establish the end-to-end percentile.

Speculative decoding is a serving algorithm, not an arbitrary draft_model switch on every hosted API. Generating two full answers and choosing one is a different technique.

Use a bounded in-extension cache first, then a shared tenant/repository-scoped cache if the measured reuse justifies it. Both levels enforce the same current permissions and request identity. A shared-cache outage may bypass reuse within the deadline; it cannot bypass authorization. Never put private suggestions in a public CDN cache.

import hashlib
import json

def completion_cache_key(scope, request):
    required = {"tenant", "user", "repository", "access_version",
                "model_version", "tokenizer_version", "prompt_version"}
    if not required <= scope.keys():
        raise ValueError("Incomplete trusted cache scope")
    payload = {
        "scope": {key: scope[key] for key in sorted(required)},
        "document_uri": request["document_uri"],
        "document_version": request["document_version"],
        "cursor": request["cursor"],
        "language": request["language"],
        "formatted_input": request["formatted_input"],
        "generation_settings": request["generation_settings"],
    }
    return hashlib.sha256(json.dumps(payload, sort_keys=True).encode()).hexdigest()

A hash key does not encrypt code, establish access or prove that a cached answer is current. Relevant reference-file changes must change the formatted input/context digest. Purge or invalidate artifacts on permission changes and follow the customer's source-retention policy.

9. Measure usefulness and cost accurately

Track displayed suggestions, accepted suggestions, retained changes at a defined interval, interruptions, defects and completed tasks. Deduplicate feedback by suggestion/event ID and respect the customer's collection policy. Do not upload full source merely to compute retention if an approved local summary suffices.

def acceptance_metrics(events):
    # One final record per shown suggestion; retention interval is defined separately.
    shown = len(events)
    accepted = sum(bool(e["accepted"]) for e in events)
    retained = sum(bool(e["accepted"] and e["retained"]) for e in events)
    return {"acceptance_rate": accepted / shown if shown else None,
            "retained_rate": retained / shown if shown else None}

A display threshold selects which outcomes are observed. Raising it may increase acceptance among shown suggestions while reducing useful changes per hour. Use an approved controlled experiment and measure coverage, defects and developer experience. Multiplying a model's uncalibrated confidence by historical acceptance does not create a calibrated probability.

Million-request worksheet

Use a separate costing scenario of 1M requests, comprising 980,000 FIM suggestions and 20,000 requested tasks. This route mix is an assumption, not the same as the earlier daily workload.

The Codestral model page supplies the FIM rates used here. The task allowance is illustrative, not a quote for a named model. Include unsuccessful calls, sandbox CPU/memory, scanners, storage, egress, review and idle capacity. Local verification is not free merely because it has no token bill. Private inference is costed from hardware utilization and operations rather than hosted rates.

10. Failure drills and release plan

1. Move the cursor and edit the buffer while a completion is in flight; stale output must not appear or apply.
2. Revoke repository access while context/index/cache work is pending; enforce the new scope.
3. Modify a file after a job snapshot; the patch must not overwrite the user's newer work.
4. Run a repository test that attempts network access or secret reads; validate the sandbox boundary.
5. Remove the type checker or time out the test runner; results must show unavailable, not passed.
6. Have generated code weaken a test; inspect whether independent checks and review detect it.
7. Lose the model/cache service; preserve editor responsiveness and provide a clear fallback.

Pilot languages with reliable parser/checker support, then expand. Compare changes by language, repository size, task difficulty and cold/warm state. A known high-severity finding fails a release gate that requires none, even if 99.8% of the overall suite passes; good acceptance does not cancel it. Keep rollback for model, prompt, context-index and display-gate versions.

Interview follow-ups

1. Why two paths? A short completion needs minimal latency and must be disposable when the cursor changes. A multi-file task needs isolated execution, durable state and stronger checks. Shared unconstrained queues let long tasks harm typing responsiveness.

2. What should happen to a late result after cancellation? Check request ID, document version, cursor and replacement range again, then discard obsolete output. Remote cancellation is an optimization; the local snapshot check provides correctness.

3. Can syntax and security scans prove the patch is safe? No. They detect particular classes of errors. Tests, dependency checks, sandboxing and review add evidence, but check results must be tied to the actual artifact and state their limitations.

4. How would you support a repository larger than the context window? Use incremental symbols/definitions, lexical or semantic retrieval, and task-specific dependency exploration. Preserve unsaved buffers and versioned references; measure whether retrieved context improves correct edits rather than simply adding tokens.

5. Why might acceptance improve while developer productivity declines? The system may suggest easy boilerplate, interrupt frequently or create code that later needs repair. Measure retained useful work, defects and task completion with an appropriate comparison.

6. When is best-of-N worth it? When the evaluated gain in completed, correctly checked tasks exceeds all candidate-generation, selection and testing costs. A selector can prefer plausible but incorrect code, so test the entire procedure.

60-second interview answer

I would separate fast inline suggestions from isolated edit-and-test jobs. The extension captures the exact buffer and cursor, retrieves only relevant permitted context, and discards stale responses before display or insertion. Larger tasks return a complete diff with checks tied to the tested version, while preserving the developer's existing work. I would enforce private-data and sandbox boundaries, then evaluate retained useful changes, defects, end-to-end latency and total cost across languages and task types.

Remember: Current context → Bounded suggestion → Checked artifact → Developer control.