Code Explanation
About prompting AI to dissect, interpret, and explain code at varying levels of detail and abstraction — from line-by-line walkthroughs to high-level architectural overviews tailored to any audience.
Introduced: AI-powered code explanation became a mainstream capability during 2022–2024, driven by the rapid advancement of large language models trained on vast code corpora. GitHub Copilot Chat (launched in 2023) brought conversational code explanation directly into developers’ editors, allowing them to highlight a function and ask “what does this do?” in natural language. ChatGPT’s code analysis capabilities — available from late 2022 onward — demonstrated that general-purpose language models could parse, interpret, and explain code across dozens of programming languages with surprising accuracy. These tools moved code explanation from a purely human skill to a human-AI collaborative process, where the quality of the explanation depends heavily on how precisely the user specifies audience, scope, and depth.
Modern LLM Status: Code explanation is a core strength of modern frontier models and one of the most reliable code-related AI capabilities available today. Models like GPT-4, Claude, and Gemini can explain code in virtually any mainstream programming language, adjust their explanations to different skill levels, identify design patterns and anti-patterns, trace execution flow, and connect implementation details to broader architectural decisions. The key differentiator in output quality is not whether the model can explain the code — it almost certainly can — but whether the prompt specifies the right audience level, the right scope of analysis, and the right output structure. Without these constraints, models default to a generic walkthrough that may be too shallow for experts or too dense for beginners.
Audience, Scope, and Depth Define the Explanation
Code explanation prompting is the practice of guiding AI models to analyze, interpret, and articulate what existing code does — and why it does it that way. Every piece of code can be explained in countless ways: a sorting algorithm can be described as a mathematical operation, a performance optimization, a beginner’s learning exercise, or a security-critical data handling step, depending entirely on who is reading the explanation and what they need to understand.
The core insight is that code explanation quality depends on specifying the AUDIENCE, the SCOPE, and the DEPTH of explanation needed. A prompt that says “explain this code” forces the model to guess at all three dimensions, producing a generic middle-ground explanation that serves nobody particularly well. But when you define who the reader is (junior developer, security auditor, non-technical stakeholder), what scope to cover (single function, module interactions, system architecture), and how deep to go (high-level summary, line-by-line walkthrough, algorithmic complexity analysis), the model produces explanations that are immediately useful and precisely targeted.
Think of it like asking a tour guide to explain a cathedral. A structural engineer wants to hear about load-bearing walls and foundation techniques. An art historian wants to discuss the stained glass and sculptural programs. A tourist wants to know the highlights and the best photo spots. The cathedral is the same — the explanation changes entirely based on the audience. Code explanation prompting works the same way: the code is fixed, but the explanation should be shaped by who needs to understand it and why.
When a model receives code without clear audience or scope instructions, it defaults to a mid-level walkthrough that restates what each line does in plain English — essentially transliterating syntax into prose without adding real understanding. Structured code explanation prompts redirect this behavior by defining the explanatory framework the model should apply: who the reader is and what they already know, which parts of the code matter most for their purpose, how much implementation detail versus conceptual context they need, whether to emphasize correctness, performance, security, maintainability, or design philosophy, and what output format (inline comments, narrative essay, structured documentation, annotated walkthrough) best serves the reader’s use case. The difference between “this function iterates over an array” and “this function implements a sliding window pattern to achieve O(n) time complexity instead of the naive O(n squared) approach, which matters here because this runs on every API request” comes down entirely to the quality of the accompanying prompt.
The Code Explanation Process
Four steps from raw code to targeted, audience-appropriate explanations
Provide the Code
Supply the code you want explained, along with any relevant context that is not visible in the snippet itself. This includes the programming language (if not obvious), the broader system the code belongs to, any external dependencies or APIs being called, and whether the code is production-ready, a prototype, or legacy code under consideration for refactoring. The more contextual information you provide upfront, the less the model has to guess — and guessing is where generic, unhelpful explanations originate.
Paste a Python function along with a note: “This is part of our payment processing pipeline. It runs on every checkout request and interacts with our Redis cache layer. The codebase uses Python 3.11 with type hints throughout.”
Specify the Audience Level
Define who will read the explanation and what they already know. A junior developer joining the team needs different context than a senior architect reviewing code for design patterns, a security auditor scanning for vulnerabilities, or a product manager trying to understand why a feature works the way it does. The audience level determines vocabulary choices, how much foundational knowledge the explanation assumes, and whether the model should explain language-level constructs or focus exclusively on business logic and architectural decisions.
“Explain this for a junior developer who knows basic Python but has never worked with async programming, Redis, or payment processing systems. Define any domain-specific terms on first use.”
Define Explanation Scope
Specify what dimensions of the code the explanation should cover. A line-by-line walkthrough produces a very different output than an architectural overview, a security analysis, a performance assessment, or a maintainability review. You can also scope the explanation to specific aspects: “focus only on the error handling logic,” “explain the data flow through this function,” or “describe how this interacts with the database layer.” Scoping prevents the model from producing an exhaustive but unfocused explanation that buries the reader in irrelevant details.
“Focus on three things: (1) the overall purpose and data flow, (2) the error handling strategy and what happens when the Redis connection fails, (3) any potential race conditions in the concurrent access pattern.”
Request Structured Output
Tell the model how to format its explanation. Unstructured prose explanations are hard to scan and reference later. Instead, specify whether you want a narrative walkthrough with headers, annotated code blocks with inline comments, a bullet-point summary, a table mapping functions to their purposes, or a combination of formats. Structured output makes explanations reusable — they can be dropped into documentation, onboarding guides, code review comments, or technical specifications without reformatting.
“Structure your explanation as: (1) A one-paragraph executive summary, (2) A section-by-section walkthrough with the code broken into logical blocks, each followed by a plain-language explanation, (3) A summary table listing each function, its inputs, outputs, and side effects.”
See the Difference
Why structured explanation prompts produce dramatically better results
Vague Prompt
Explain this code.
This code defines a function that takes a list and returns a new list. It uses a loop to iterate through the elements and applies some filtering logic. The function also handles errors using a try-except block.
Structured Explanation Prompt
Explain this Python function to a junior developer who understands basic Python but has never worked with list comprehensions or exception handling. Cover: (1) what the function does at a high level, (2) why the filtering logic works the way it does, (3) what the try-except block protects against. Use a paragraph summary followed by an annotated code walkthrough.
Summary: This function filters a list of user records to return only active accounts with valid email addresses. It exists because upstream data sources sometimes contain malformed entries, and downstream services will fail if they receive invalid records.
Line 3–5 (List Comprehension): This is a compact loop syntax. Instead of writing a for-loop and appending to a new list, Python lets you filter and transform in a single line. The condition user.is_active and validate_email(user.email) keeps only records that pass both checks.
Line 7–9 (Try-Except): The try-except block catches ValidationError specifically. If any single record has a malformed email that crashes the validator, the function logs that record and continues rather than failing the entire batch.
Practice Responsible AI
Always verify AI-generated content before use. AI systems can produce confident but incorrect responses. When using AI professionally, transparent disclosure is both best practice and increasingly a legal requirement.
48 US states now require AI transparency in key areas. Critical thinking remains your strongest tool against misinformation.
Code Explanation in Action
See how targeted prompts unlock deeper code understanding
“Explain this binary search implementation to someone who has completed an introductory programming course but has never studied algorithms formally. Cover: (a) what problem binary search solves and why it is faster than checking every element, (b) a step-by-step walkthrough of how the algorithm narrows down the search range on each iteration, (c) why the code uses integer division for the midpoint calculation, (d) what happens when the target element is not in the array. Use a real-world analogy to introduce the concept before diving into the code. Format the walkthrough as numbered steps, each referencing the specific lines of code involved.”
This prompt succeeds because it specifies the exact knowledge boundary of the reader — they know basic programming but not algorithms. This tells the model to explain algorithmic concepts from scratch while skipping explanations of loops and variables. By requesting an analogy first, the prompt ensures the reader builds intuition before encountering code. The four specific coverage areas prevent the model from producing a surface-level “this searches for an element” explanation and instead force it to address the performance reasoning, edge cases, and implementation subtleties that actually build understanding.
“Analyze this authentication middleware from a security perspective. The audience is a senior security engineer conducting a code audit. For each function in the module, explain: (a) what security-critical operation it performs, (b) what attack vectors it is designed to prevent, (c) any assumptions it makes about input sanitization or trust boundaries, (d) potential vulnerabilities or weaknesses you can identify, including timing attacks, injection risks, and improper error disclosure. Do not explain basic programming concepts — assume deep expertise in both the language and security engineering. Format as a security audit report with severity ratings for any identified issues.”
This prompt works because it establishes a specific professional lens — security audit — and explicitly tells the model not to waste space on programming basics that the audience already knows. By requesting attack vector analysis, trust boundary identification, and severity ratings, the prompt transforms a generic code walkthrough into a structured security review document. The specificity of the vulnerability categories (timing attacks, injection, error disclosure) guides the model to check for particular classes of issues rather than producing a vague “this looks secure” assessment.
“This is a legacy module from a 15-year-old Java codebase. The original authors are no longer with the company and there is no existing documentation. Explain this code for a team of mid-level developers who need to maintain and eventually refactor it. Cover: (a) the overall purpose and business logic the module implements, (b) the data flow from input to output, identifying all external dependencies and side effects, (c) any design patterns used (even if implemented inconsistently), (d) technical debt items and code smells with specific line references, (e) implicit assumptions the code makes that are not documented anywhere. Structure the output as a technical reference document with a summary section, a detailed walkthrough section, and a maintenance notes section listing the most important things a new maintainer should know.”
This prompt addresses one of the most common and highest-value code explanation use cases: making undocumented legacy code understandable. By specifying that the original authors are unavailable, the prompt tells the model it cannot rely on any external context and must derive all understanding from the code itself. The five analysis dimensions cover both what the code does (purpose, data flow) and what a maintainer needs to watch out for (tech debt, hidden assumptions). The three-part output structure produces a document that can be immediately added to the team’s internal documentation, making the explanation a durable knowledge artifact rather than a one-time chat response.
When to Use Code Explanation
Best for making existing code understandable to specific audiences
Perfect For
Generating audience-appropriate explanations of existing codebases for new team members, helping them understand unfamiliar systems without requiring hours of one-on-one walkthroughs from senior developers.
Producing structured explanations of complex pull requests or modules before review sessions, ensuring all reviewers understand the intent, design decisions, and trade-offs embodied in the code.
Deciphering undocumented or poorly documented legacy systems where the original authors are unavailable, extracting business logic and architectural intent from the implementation itself.
Using AI-generated explanations to learn new programming languages, frameworks, design patterns, or algorithmic techniques by studying real code with explanations calibrated to the learner’s current level.
Skip It When
When the code uses clear naming, standard patterns, and is already well-commented, AI explanation adds little value. Well-written code with descriptive variable names and small, focused functions often explains itself better than any external summary.
If the goal is to confirm whether code behaves correctly under all conditions, testing and formal verification are more reliable than AI explanation. Models can misinterpret subtle logic and present confident explanations of code that actually contains bugs.
When the code contains trade secrets, classified algorithms, or highly sensitive intellectual property that should not be sent to external AI services, even for explanation purposes. Use local models or manual review instead.
When you need precise runtime performance data rather than an explanation of what the code does, profiling tools and benchmarks provide quantitative answers that AI explanations cannot reliably deliver.
Use Cases
Where code explanation delivers the most value
Code Review Documentation
Generating structured explanations of complex changes before code review sessions, ensuring reviewers understand the intent, trade-offs, and design decisions behind each modification without requiring the author to walk through every line verbally.
Onboarding New Developers
Creating audience-calibrated walkthroughs of critical system components for new hires, reducing ramp-up time by providing explanations that match their experience level and highlight the most important concepts for their role.
Technical Debt Assessment
Analyzing legacy or inherited codebases to identify code smells, anti-patterns, implicit assumptions, and areas where the implementation has drifted from best practices — producing actionable reports that prioritize refactoring efforts.
Educational Content Creation
Generating tutorial-quality explanations of code examples for courses, blog posts, documentation, and learning platforms — with explanations that progressively build understanding and connect code constructs to broader programming concepts.
Debugging Assistance
Explaining what code is supposed to do versus what it actually does when a bug is present, helping developers understand the gap between intent and implementation by tracing data flow and identifying where logic diverges from expectations.
Architecture Documentation
Producing high-level architectural explanations from code, describing how modules interact, what design patterns are employed, where the system boundaries are, and how data flows through the application — creating living documentation derived directly from the source.
Where Code Explanation Fits
Code explanation bridges the gap between writing code and understanding code at scale
AI-generated code explanations are powerful for building understanding, but they should always be paired with verification. Models can confidently explain code incorrectly — especially when dealing with subtle concurrency issues, complex state machines, or language-specific edge cases. Use code explanation to build your initial understanding, then verify critical claims by tracing the logic manually, writing tests, or running the code with specific inputs. The combination of AI-generated explanation and human verification produces deeper understanding than either approach alone, because the explanation gives you a hypothesis to test and the verification process reveals gaps in both your understanding and the model’s analysis.
Related Techniques
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Explore Code Explanation
Apply structured code explanation techniques to your own projects or build audience-targeted prompts with our tools.