Copilot from Completion to Plan Mode: Will interviewers value "task decomposition ability" or "coding ability" more?

As generative AI deeply embeds into the full software development lifecycle, the underlying evaluation standards for technical interviews are undergoing irreversible restructuring. With GitHub Copilot instantly generating lines of syntactically perfect code, rote syntax memory and coding speed are no longer scarce resources; "task decomposition" and "logic orchestration" are replacing traditional "hand-coding" as the new benchmarks defining the value of senior engineers. Amid this shift, many candidates remain stuck at the basic stage of using AI for simple auto-completion, overlooking the differentiating advantage interviewers truly seek: how candidates master advanced tools like VS Code Copilot Agent to transform vague, complex business requirements into precise, executable engineering blueprints. This article moves beyond basic Prompt techniques to analyze the core value of Copilot Plan Mode in practical interviews, revealing how to utilize this "architect mode" to break down massive system design challenges into rigorous sub-task sequences. We will detail everything from Copilot Edits workflow configuration to deconstruction strategies for complex algorithm problems, demonstrating how to actively direct AI for logic verification and implementation rather than passively relying on generated results. This is not just about tool usage, but a mindset upgrade—in assisted algorithm interviews, you must prove you are no longer just a code executor, but a technical commander capable of using AI to solve complex system problems with clear Copilot task decomposition skills. Mastering this leap from "completion" to "planning" is key to winning the next generation of technical interviews and demonstrating superior engineering literacy.

Why "Task Decomposition" Is Replacing "Hand-Coding" as the Core of Interviews?

In traditional coding interviews, candidates often felt anxious about forgetting the specific parameter order of an API or missing a semicolon. However, with the proliferation of AI-assisted programming tools, this anxiety is becoming obsolete. In an era where Copilot can instantly generate dozens of lines of syntactically correct boilerplate code, "writing code" is no longer a scarce ability; the logical design ability to define "what the code should do" is the expensive asset.

Syntax Is Cheap, Logic Is Expensive

The focus of interviewers is undergoing a fundamental shift. In the past, they tested whether you had memorized the standard library; now, they value your ability to harness AI to solve complex problems. This is effectively a test of "Task Decomposition" skills.

When facing a vague interview question (e.g., "Design a concurrency-supported rate limiter"), relying solely on AI "autofill" often leads to disastrous consequences—the code strays further down the wrong logic path or hallucinates on edge cases. Research shows that when dealing with medium- to high-difficulty programming tasks, attempting to have AI generate complete code in one go often leads to failure; conversely, developers who can break down complex tasks into simpler subtasks and guide AI step-by-step achieve significantly higher efficiency and success rates (Reference: Evaluating the Usability of Code Generation Tools).

From "Completion" to "Architecture": The Role of Copilot Plan Mode

To adapt to this demand, the tools themselves are evolving. Without understanding the evolution of tools, it is difficult to demonstrate a differentiated advantage in interviews.

• Traditional Copilot (Autofill/Chat): Mainly plays the role of a "super keyboard." It reacts quickly but lacks a big-picture view, making it suitable for single-line code patches.
• Copilot Plan Mode (Agent): This is the "Architect" mode for interviews. According to the introduction to GitHub Copilot Agent Mode, this mode goes beyond just answering questions; it can autonomously decide which files to edit, which terminal commands to run, and verify the correctness of the code.

In an interview, enabling Plan Mode means you are no longer passively accepting AI suggestions but actively formulating a plan. Interviewers want to see how you use this "Agent" to transform a general requirement into a series of executable Todo Lists, and then verify and implement them step by step.

The Goal of This Article

This shift from "hand-coding" to "commanding AI" requires candidates to possess a brand-new mental model. It is not just about how to write Prompts, but about how to design systems. This article will delve into how to use Copilot Plan Mode to demonstrate your superior logical thinking abilities in interviews—by making AI your pair programming partner through precise task decomposition, rather than your replacement. We will show how to systematically master this core skill of the new era of interviews, from environment configuration to practical problem-solving.

Understanding Terminology and Environment: Differences between Agent, Edits, and Plan Mode

As GitHub Copilot's features continue to iterate, its terminology and user interface are also evolving rapidly, causing confusion for many candidates during interview preparation. To utilize AI for complex "task decomposition," one must first clarify the hierarchical relationship of the tools and ensure your development environment is correctly configured to support advanced features.

Core Concept Differentiation: From Chat to Agent

In the current VS Code ecosystem, Copilot offers three main interaction modes. These are not simply a stack of features, but correspond to completely different workflow depths:

1. Copilot Chat (Panel/Sidebar)
   This is the most basic conversation mode, similar to embedding a ChatGPT within the IDE. It excels at explaining concepts, generating code snippets, or answering syntax questions (Ask Mode), but it usually cannot directly modify your codebase, requiring you to manually copy-paste or click to insert.
2. Inline Chat (Cmd/Ctrl + I)
   This is a "single edit" mode targeting the current file. You select a piece of code, input a command (such as "refactor this function to handle null pointers"), and Copilot will generate a diff directly in the editor for you to accept or reject. As noted in relevant analysis, Edit Mode is suitable for making specific, small-scope changes while maintaining control.
3. Copilot Edits and Agent (Plan Mode)
   This is the core for demonstrating "task decomposition capabilities" in interviews. Copilot Edits is a new View, while Agent Mode is a capability running within that view.

• • Characteristics of Agent: Unlike the "one question, one answer" style of Inline Chat, the Agent possesses context awareness and multi-step execution capabilities. It can work across multiple files, automatically run terminal commands, and even perform self-healing when encountering errors.
  • Plan Mode: In Agent mode, when you input a complex requirement, it does not write code immediately but first generates a step-by-step "implementation plan" (Plan). This is precisely the key link where interviewers assess logical thinking—are you coding blindly, or are you guiding the AI to architect a solution?

Environment Configuration Checklist

Since Agent and Plan Mode are still in a rapid iteration phase (Preview), the standard stable version of VS Code may not have this feature enabled by default. To use it smoothly during interviews or practice, please check your environment against the following standards:

• VS Code Version: It is recommended to use the VS Code Insiders version (usually requiring 1.98.0 or higher), as the latest Agent features are often released there first.
• Extensions: Ensure that the latest versions of the GitHub Copilot and GitHub Copilot Chat extensions are installed and enabled.
• Feature Flags:
  If you cannot find the "Agent" option in the interface, you may need to manually enable the configuration. Look for and enable the following options in Settings or settings.json:
• • github.copilot.editor.enableAgent: Set to true.
  • Some versions may require activation via github.copilot.chat.agent.enabled.
• Launch Method:
  After configuration is complete, open the dedicated view by entering "Copilot Edits" via the Command Palette (Ctrl/Cmd + Shift + P). In the mode dropdown menu below the input box, you should be able to see the "Agent" option. As stated in the official Microsoft blog, after selecting Agent mode, Copilot will act as an autonomous pair programmer capable of executing multi-step coding tasks.

Ensuring the environment is ready is the prerequisite for demonstrating technical strength, avoiding awkward situations caused by tool version issues during the interview process. Next, we will specifically analyze how to precisely select these three modes in different interview stages.

Comparison of Applicable Scenarios for the Three Modes

In the time-critical environment of an interview, choosing the wrong AI interaction mode will not only waste time but may also make the interviewer feel that you lack control over your tools. GitHub Copilot currently provides three main interaction forms: Chat, Edits, and Agent (Plan Mode).

Understanding their boundaries is the first step to demonstrating "efficient engineering ability." Here is a quick decision guide for interview scenarios:

1. Chat Mode: External Knowledge Brain (Ask)

When to use: When you need "ideas" rather than "code implementation."
In an interview, if you are unsure about the time complexity of an algorithm, or have forgotten the specific syntax of a language feature (e.g., Python's heapq usage), asking directly in the sidebar Chat is the fastest way.

• Interview Command Example: "Explain the time complexity differences between QuickSort and MergeSort in this context."
• Note: Do not try to use it to generate long, complete solutions, because the generated code usually requires manual copy-pasting, which can easily interrupt the rhythm of your demonstration.

2. Edits Mode: Precision Surgery (Edit)

When to use: When you already know exactly what to do but don't want to manually type boilerplate code.
This is the evolved version of the traditional "Inline Chat." When you need to optimize an existing code segment, add comments, or convert it to another writing style, use Edits mode (usually invoked via Cmd+I or Ctrl+I).

• Interview Command Example: Select a piece of brute-force code and input "Refactor this function to use a hash map for O(n) complexity."
• Advantage: It directly displays changes in the editor with a Diff view, allowing you to quickly Review and accept, which is very suitable for demonstrating your control over code details. As pointed out in GitHub Copilot's related analysis, this mode is suitable for "applying specific small changes while maintaining control."

3. Agent / Plan Mode: Comprehensive Planning (Plan)

When to use: This is the core weapon in interviews. When you receive a complex problem (such as "design a simple rate limiter" or "implement a text editor supporting undo"), do not rush to write code.
Agent mode (often reflected as the Agent option under the Copilot Edits interface in VS Code) can understand your high-level intent and translate it into a specific execution plan.

• Interview Command Example: "Create a plan to implement a Rate Limiter class that supports sliding window algorithm. Handle thread safety."
• Core Value: It not only generates code but, more importantly, possesses "Task Breakdown" capabilities. It will first generate a Todo List (Plan), letting you confirm the logic with the interviewer, and then execute the code. According to Visual Studio Magazine's report, Agent mode transforms Copilot from a passive chatbot into an active coding agent capable of achieving goals through multi-step reasoning and execution.

Tactical Summary:
In the opening stage of the interview, be sure to use Agent / Plan Mode to demonstrate how you translate ambiguous requirements into engineering plans; during the specific code implementation and optimization stage, switch back to Edits Mode to maintain high-precision control; use Chat Mode only when stuck or needing an explanation.

Interview Practice: How to Use Plan Mode for "Task Decomposition"

In algorithm or system design interviews, generating code directly is often a "major taboo." This not only makes the interviewer feel you lack a thought process but also easily leads to code logic deviating from requirements due to AI "hallucinations." By utilizing Copilot's Plan Mode, we can establish a standard "Prompt -> Plan -> Execute" workflow, transforming the "black box" code generation process into a transparent display of "task decomposition."

"Prompt -> Plan -> Execute" Workflow

The core value of Plan Mode lies in forcing the separation of "thinking" and "execution." In an interview scenario, you should not directly press "Generate Code," but instead first use the Agent to generate an executable Todo List or Implementation Plan.

1. Prompt: Input the problem description or requirements, explicitly requesting an "implementation plan" rather than the final code.
2. Plan: The AI will generate a step-by-step plan based on the context (e.g., defining states, initializing variables, handling boundary conditions). At this point, you can show this plan to the interviewer and ask: "Does this approach align with your expectations?"
3. Execute: After confirming the plan is correct, then ask the AI to generate code for each step.

This task decomposition strategy has been proven to improve the efficiency of solving complex problems. It not only reduces the code error rate but also demonstrates to the interviewer that you possess a clear engineering mindset, rather than just being a "fill-in-the-blanks" user relying on tools.

Verifying Logic and Communication Value

The greatest strategic advantage of using Plan Mode lies in Communication. In traditional interviews, candidates need to write pseudo-code on a whiteboard to verify their ideas; in AI-assisted interviews, the generated Plan is your "pseudo-code."

By displaying the Plan, you can:

• Correct Deviations: If the AI's understanding of the first step is incorrect (e.g., misunderstanding boundary conditions), you can correct the plan via natural language before generating code, which is much more efficient than debugging a pile of erroneous code.
• Demonstrate Control: As demonstrated in relevant tutorials, the Plan Agent allows users to review and iterate on requirements before execution. This interactive process is exactly the embodiment of the "Review Mechanism" of senior engineers.

The Critical Role of Context Management

The accuracy of Plan Mode relies heavily on Context. In an interview, simply pasting the question is often not enough; you also need to ensure the Agent understands the current engineering environment.

• Explicitly Add Context: Use @workspace or manually add relevant files (such as the predefined ListNode class, utility function libraries) to the conversation context.
• Lock Scope: For system design questions, ensure that generated multiple files (such as Model, Controller) are kept in the Agent's working memory to prevent it from "forgetting" previous interface definitions in subsequent steps.

Mastering this workflow gives you the key to transforming AI tools from "cheating devices" into "productivity multipliers." Next, we will demonstrate this process through a specific LeetCode case.

Case Study: From LeetCode Problem to Executable Code

In actual interviews, generating code directly is often seen as "cheating" or a lack of communication. By utilizing Copilot's Plan Mode, we can transform the problem-solving process into an interactive demonstration of "task decomposition skills." Below, taking the classic LeetCode 56. Merge Intervals as an example, we demonstrate how to complete the entire process from understanding the problem to code implementation using Agent mode.

Step 1: Context Injection and Initial Instructions (Context Injection)

First, do not simply throw the problem into the chat box and expect the final answer. We need to simulate the "problem restatement and confirmation" phase of an interview.

1. Switch Modes: Ensure you are in VS Code's Copilot Agent mode (usually select Plan or Agent in the Chat panel, depending on version updates; refer to Introducing GitHub Copilot agent mode).
2. Input Problem: Paste the full problem description into the input box.
3. Send Instruction: Append a key constraint instruction to force the AI to stop before "coding."

Recommended Prompt:

"Here is the problem description for 'Merge Intervals'. Please do not generate code yet. Instead, analyze the requirements and provide a step-by-step Implementation Plan and a list of potential Edge Cases we need to handle."

Step 2: Review and Iterate Plan (Plan Validation)

Copilot will return a structured Todo List. This step corresponds to the "explaining the thought process" in an interview.

Example of Copilot Generated Plan (Simulated):

1. Input Validation: Check if the input array is empty or null.
2. Sorting: Sort the intervals based on the start time (intervals[i][0]).
3. Initialization: Create a list merged to store the result.
4. Iteration: Loop through the sorted intervals.

• • Compare current interval with the last one in merged.
  • If they overlap, merge them by updating the end time.
  • If not, append the current interval to merged.

1. Return: Convert list to array and return.

Key Operation:
At this point, you need to review this plan just like reviewing a colleague's design document. Suppose you find that the plan does not specify the logic for determining "overlaps," or fails to mention time complexity; you should immediately engage in multi-turn dialogue iteration instead of generating code.

Correction Instruction Example:

"The plan looks good, but please specify the time complexity for the sorting step. Also, clarify the condition for 'overlapping'—does [1,3] and [3,5] count as overlapping?"

This interaction demonstrates your sensitivity to algorithm complexity (e.g., O(N log N)) and your rigor regarding edge conditions.

Step 3: Generate and Validate Code (Execution)

Once the plan is confirmed to be correct, the Copilot Agent will generate code based on the locked context. According to the demonstration in Plan agent in VS Code, Agent mode can execute the previously determined steps one by one, ensuring that all requirements (including the edge conditions you just added) are met.

Final Instruction:

"The plan is solid. Please generate the Solution class in Java/Python following these steps."

The generated code will map directly to the plan steps just discussed. At this point, you can explain to the interviewer: "As per the plan we discussed, Arrays.sort is used here, followed by a single pass to complete the merge..."

💡 Snippet Opportunity: Interview-Specific "Planning" Template

To quickly trigger this behavior under high-pressure interview conditions, it is recommended to save the following template in your common Prompt library. This helps you consistently output high-quality problem-solving strategies rather than just a pile of code.

# Role
Technical Interview Candidate

# Task
Solve the following algorithmic problem: [PASTE PROBLEM HERE]

# Constraints
1. NO CODE FIRST: Do not output the full solution code immediately.
2. Plan Mode: Generate a numbered "Implementation Plan".
3. Complexity: Analyze Time and Space complexity in the plan.
4. Edge Cases: List at least 3 edge cases (e.g., empty input, single element, duplicates).

# Goal
Wait for my approval of the plan before writing the actual code.

By doing this, you not only use AI to solve the problem, but more importantly, you demonstrate to the interviewer the workflow expected of a senior engineer: Think first, then plan, and finally execute. As relevant research points out, decomposing complex tasks into subtasks (Task Decomposition) can significantly improve the success rate of code generation and user experience (refer to Evaluating the Usability of Code Generation Tools).

Advanced Application: System Design and Multi-file Context

When an interview enters the System Design or complex project building phase, the challenge candidates face is often no longer the algorithmic implementation of a single function, but how to organize code structure, manage module dependencies, and handle cross-file logic. This is the weakness of the standard Copilot Chat, but it is the highlight moment where Agent Mode (Plan Mode) truly demonstrates "architectural thinking."

Breaking Single-file Limitations: From "Completion" to "Architecture"

Traditional Copilot Chat is often limited to the currently opened file or a minimal amount of context reference. When you ask it to "design a rate limiter," it might pile up hundreds of lines of code in a single code block, or fabricate non-existent reference paths (Hallucination). This kind of performance will make interviewers question your ability to handle large-scale projects.

In contrast, GitHub Copilot Agent Mode is designed to be able to autonomously determine the files that need editing, and even build an entire application structure from scratch. It not only understands the syntax of a single file but can also perceive the topology of the entire Workspace. In system design interviews, this means you can use it to quickly build multi-file scaffolding, demonstrating clear Separation of Concerns.

Practical Demo: Building "Distributed Rate Limiter" Scaffolding

Suppose the interview question is "Design and implement a rate limiter middleware based on the Token Bucket algorithm." Instead of scrambling to manually create files, it is better to utilize Agent Mode's task decomposition capability for Project Scaffolding.

You can input the following Prompt:

"Create a project structure for a Token Bucket Rate Limiter in Go. I need separate files for the main server, the strategy logic, and the storage interface. Initialize the modules."

Agent Mode will then execute an Architecture Decomposition similar to the following:

1. Create file structure: Automatically generate main.go, bucket.go, store.go, middleware.go.
2. Define interfaces: Define storage interfaces in store.go instead of hardcoding a Redis implementation.
3. Handle dependencies: Automatically run terminal commands (such as go mod init) to initialize the project environment.

This capability is highly consistent with the Task Decomposition principle: by breaking down complex system requirements into database schemas, API interfaces, and business logic modules, what you demonstrate to the interviewer is not just coding speed, but the logical organization of system design.

"Working Set" Management and Context Consistency

In multi-file collaboration, the most fatal problem is Context Loss. For example, you modified a function signature in utils.js but forgot to update controller.js which calls that function.

Plan Mode introduces a concept similar to a "Working Set," capable of performing Cross-file Refactoring. When you request to "change the rate limiting strategy from memory to Redis storage," Agent Mode will:

• Scan all files referencing the storage interface.
• Automatically update the implementation in store.go.
• Synchronously modify the initialization code in main.go.
• Verify whether the modified code passes compilation or tests.

According to the documentation description of GitHub Copilot features, Agent Mode can ensure code correctness through Iterative remediation. If the code it generates causes compilation errors, it will automatically read the error logs and attempt to fix them until the task is completed. This "self-healing" feature is particularly valuable in the high-pressure environment of an interview, as it can significantly reduce time wasted on low-level spelling errors or import errors.

Interview Strategy Summary

When using Plan Mode during the system design phase, please follow the strategies below to maximize E-E-A-T (Expertise and Trustworthiness):

• Plan first, generate later: Do not let the AI write code directly; first ask it to list the file structure plan and confirm the architectural rationality with the interviewer.
• Explicit context management: Utilize @workspace or manually add key design documents (such as requirements in Markdown format) to the context to ensure the Agent understands global constraints.
• Verify consistency: Utilize Agent Mode's terminal execution capability to request it to run simple test scripts, proving that the calling logic between multiple files is smooth.

In this way, you upgrade Copilot from a simple "code completion tool" to your "pair programming architect," demonstrating your ability to master complex systems.

Pitfall Avoidance Guide: AI Hallucinations and the Interviewer's "Anti-Cheating" Assessment

In the high-pressure environment of an interview, GitHub Copilot's Plan Mode (Agent Mode) seems like a "lifesaver" capable of automatically solving complex problems, but it also introduces new risks. Many candidates fall into the predicament of "promises not matching reality" due to over-reliance on AI-generated plans. The focus of interviewers is now shifting from pure "code output" to the monitoring and correction capabilities regarding AI behavior.

The Hallucination Trap of the "Perfect Plan"

The most dangerous aspect of Copilot Agent Mode is that it often generates a "perfect plan" that looks logically rigorous and detailed in steps, but may be full of holes during actual execution. This phenomenon is commonly referred to as AI Hallucinations, where AI-generated content lacks contextual basis or is completely fictional.

In interview scenarios, common Plan Mode failure scenarios include:

• Over-engineering: For a simple algorithm problem, the AI might plan out complex class structures or introduce unnecessary third-party libraries, leading to a surge in code volume and difficulty in debugging.
• Context Loss and Misoperation: Although Agent Mode aims to handle multi-file editing, it may still modify the wrong files or get stuck in a "reasoning loop" during planning, repeatedly attempting wrong repair paths without success. According to Microsoft's Agent Mode introduction, while the system provides an undo function, frequent code rollbacks in a time-critical interview will greatly consume time and disrupt the rhythm.
• Logically Self-Consistent but Incorrect Results: The AI might confidently list an algorithm logic that cannot pass edge test cases. If you do not discover this through manual review before generating code, subsequent debugging will become extremely painful.

"Anti-Cheating" in the Eyes of Interviewers: Blind Obedience is a Red Line

When interviewers allow the use of AI tools, they are actually conducting an invisible "anti-cheating" assessment. "Cheating" here does not refer to using the tool itself, but rather "Outsourcing your brain".

Interviewers will keenly pick up on the following signals as "unqualified" red lines:

1. Zero-Review Click-through: When Plan Mode generates a task list, do you click "Execute" directly without even looking at it?
2. Unexplainable Complexity: When the interviewer asks, "Why handle this in three steps here?", if you can only answer "Because that's how the AI wrote it," this directly exposes your lack of control over the task.
3. Passive Waiting: When the Agent gets stuck at a certain step or generates incorrect code, do you just stare blankly at the screen, or mechanically retry the same Prompt?

The correct response posture is: You must be the Editor-in-Chief of the Plan, while the AI is merely the Draft Writer. After using Plan Mode to generate steps, you need to spend 30 seconds quickly verifying its feasibility and be prepared to explain to the interviewer why this plan is correct.

Intervention Strategies When Stuck in a Deadlock

Even if you have done a perfect breakdown, the AI may still "jam" during the execution phase. Research shows that breaking complex tasks into subtasks can improve success rates, but Copilot still inevitably makes mistakes when handling complex scenarios. Therefore, you need to be prepared with two intervention plans:

• Plan A: Manual Intervention
  If the Agent introduces obvious syntax errors or logical loopholes while modifying a function, do not attempt to "persuade" it to correct them by writing longer Prompts. Terminate the Agent directly and manually take over the keyboard for corrections. This not only saves time but also demonstrates to the interviewer that you possess solid Coding skills and do not rely entirely on AI.
• Plan B: Degrade to Edit Mode
  If Plan Mode persistently fails to understand the overall architecture (e.g., repeatedly modifying the wrong configuration files), you should decisively switch back to the standard completion mode (Edit Mode) or single-turn conversation mode. As officially suggested, for well-defined, smaller-scoped tasks, the traditional edit mode is often more efficient and less error-prone than Agent Mode.

Remember, interviewers not only value results but also value your robustness when tools fail. Being able to identify AI hallucinations and quickly control the situation through manual intervention is the core competitiveness in the era of "human-machine collaboration".

Summary: Reshaping Your Interview Strategy with AI Assistance

Let's return to the initial question of the article: Do interviewers value "task decomposition ability" or "coding ability" more? The answer is already clear—in an era where AI-assisted programming has become the norm, Task Decomposition is the dividing line that distinguishes junior engineers from senior engineers.

Interviewers are no longer testing whether you remember the parameter order of every API, but whether you possess the "systems thinking" to harness AI tools to solve complex problems. As related research indicates, when facing high-difficulty tasks, developers who can decompose complex problems into simple sub-tasks and perform step-by-step Prompting demonstrate significantly higher solution efficiency and code quality than those attempting to reach the solution "in one go."

Refactoring Your Interview Workflow: The Human-AI-Human Loop

To demonstrate this high-level capability in interviews, you need to transform from a mere "coder" into a "technical lead," establishing the following collaborative loop:

1. Human Sets Strategy (Defining Strategy - Your Core Value)

• • Responsibility: Before writing code, align requirements with the interviewer (and the AI). Clarify inputs/outputs, Edge Cases, and core algorithm logic.
  • Action: Use natural language to describe your problem-solving approach, or utilize Copilot's Plan Mode to generate a detailed Todo List. This step is a critical moment to demonstrate your logical clarity and serves as the first line of defense against AI "hallucinations."

1. AI Handles Tactics (Execution - The Tool's Value)

• • Responsibility: Leverage Copilot to quickly generate Boilerplate code, convert data structures, or implement specific utility functions.
  • Action: Outsource tedious syntax details to the AI. At this point, your role is that of a "supervisor," ensuring the AI strictly follows the Plan you set, rather than diverging arbitrarily.

1. Human Verifies Result (Acceptance - Your Safety Net)

• • Responsibility: Review the code for correctness, security, and performance.
  • Action: Do not blindly accept all of the AI's suggestions. You need the ability to spot logical loopholes (such as infinite loops, memory leaks) at a glance and be able to explain every line of code—because the interviewer is ultimately challenging you, not Copilot.

Make "Prompting for Plans" a Daily Deliberate Practice

From now on, please change your coding practice habits. Do not rush to type def solution the moment you see a problem, and do not attempt to use a single Prompt to get the AI to provide a perfect answer instantly.

The new training goal is: Practice how to describe a "plan."

• Before: Directly asking the AI to write "implementation code for LRU Cache."
• After: Practice writing a clear piece of pseudocode or comment, requiring the AI to first generate implementation steps (e.g., "1. Define doubly linked list node; 2. Use hash map to map Key to node; 3. Implement get/put operations and handle capacity overflow").

If the plan generated by Copilot is chaotic, it indicates that your understanding of the problem is not thorough enough. Plan Mode is not just a tool for writing code; it is a mirror that reflects the clarity of your thinking.

In future interviews, excellent candidates will not be penalized for using AI; on the contrary, those who can confidently open Plan Mode and, through precise decomposition and instructions, direct the AI to build high-quality, maintainable systems within a short timeframe, will win the genuine favor of interviewers.