Make Sense of Legacy Code with AI Explainers

Tested prompts for ai to understand legacy code compared across 5 leading AI models.

BEST BY JUDGE SCORE Claude Haiku 4.5 9/10

You're staring at a codebase that predates your tenure, written in a language you half-know, with no documentation and variable names like 'x2' and 'tmpFlag'. Your job is to fix it, extend it, or migrate it, and you have no idea where to start. That's the exact problem AI code explainers solve.

Modern AI models, including GPT-4o, Claude, and Gemini, can read raw source code and return plain-English explanations of what it does, why it likely does it that way, and what risks are hiding inside it. You paste the function, class, or module. The AI tells you the story behind it. No archaeology required.

This page shows you a tested prompt designed specifically for legacy code comprehension, four model outputs side by side, and a comparison table so you can pick the right tool for your situation. Below that you'll find practical guidance: when this approach works, when it fails, and how to get better explanations out of any model you use.

When to use this

AI-assisted legacy code explanation is the right move when you need to understand unfamiliar code faster than reading it line by line allows. It works best when the code is self-contained enough to paste in one chunk, the logic is non-trivial but not spread across dozens of interdependent files, and your goal is comprehension first, modification second.

  • You've inherited a codebase and need to get up to speed before your first sprint ends
  • A critical bug is buried in a 300-line COBOL or Perl function nobody on the team has touched in years
  • You're writing a migration spec and need to document what the current system actually does before replacing it
  • A pull request touches legacy code and you need to review it without spending an hour reverse-engineering the context
  • You're onboarding a new engineer and want to generate readable explanations to attach to internal wiki pages

When this format breaks down

  • The logic spans dozens of files with deep interdependencies: pasting a single function gives the AI incomplete context, and the explanation will be confidently wrong about side effects it cannot see
  • The codebase contains proprietary algorithms or sensitive data you cannot legally paste into a third-party model endpoint
  • You need a formal, auditable specification: AI explanations are probabilistic and can miss edge cases, so they should not substitute for verified technical documentation in regulated industries like aviation or medical devices
  • The code is heavily obfuscated or minified with no structure: models struggle with intentionally scrambled logic and tend to hallucinate plausible-sounding but incorrect explanations

The prompt we tested

You are a senior software engineer specializing in reverse-engineering and explaining legacy code. Analyze the code or codebase description provided and produce a clear, structured explanation that helps the reader understand what it does, how it works, and why it was likely built this way.

Follow these rules:
Structure the response with these sections: (1) High-Level Purpose (2-3 sentences), (2) Key Components & Responsibilities (bulleted), (3) Control Flow / Data Flow Walkthrough (step-by-step), (4) Suspicious or Outdated Patterns (bulleted, flag deprecated APIs, anti-patterns, or risky logic), and (5) Questions to Ask the Original Author. Use plain language, define any obscure terms inline, and keep the total output under 500 words.

Legacy code or codebase description to analyze:
Here is a 300-line Perl CGI script from 2003 that handles our company's internal expense report submissions. It uses DBI to talk to an Oracle database, has inline HTML generation, and calls a subroutine named 'mangle_approver' that I can't figure out. No comments, no documentation, and the original developer left 15 years ago.

Produce the explanation now.

Model comparison

ModelQualityCostLatencyTokens
Claude Opus 4.7 9 $0.1114 24.8s 404 / 1404
Claude Haiku 4.5 9 $0.0041 9.4s 283 / 766
GPT-5.4 9 $0.0143 21.6s 251 / 912
Gemini 2.5 Pro 8 $0.0016 19.1s 261 / 248
Grok 4.1 Fast Reasoning 9 $0.0004 10.4s 397 / 627

Outputs

Claude Opus 4.7 9/10
# Legacy Perl CGI Expense Report Script Analysis

## 1. High-Level Purpose

This is a classic early-2000s web application: a single Perl CGI (Common Gateway Interface) script that renders an HTML form for employees to submit expense reports, validates input, and persists records to an Oracle database via DBI (Perl's DataBase Interface module). It likely handles the full round-trip—display form, accept POST, insert rows, show confirmation—in one monolithic file.

## 2. Key Components & Responsibilities

- **CGI request handling**: Probably uses `CGI.pm` or raw `%ENV`/`STDIN` parsing to read form parameters.
- **DBI/Oracle connection block**: Opens a connection to Oracle (likely via `DBD::Oracle`), using hardcoded or environment-based credentials.
- **Inline HTML generation**: Uses `print` statements or heredocs (`<<HTML`) to emit markup directly, mixing presentation with logic.
- **Input validation**: Ad-hoc regex checks on amounts, dates, employee IDs.
- **SQL INSERT logic**: Builds statements to write into expense-related tables.
- **`mangle_approver` subroutine**: Almost certainly transforms an approver identifier before lookup or insert. Common possibilities:
  - Strips domain from an email (`jdoe@corp.com` → `jdoe`)
  - Converts a manager's name to an LDAP/NT username
  - Applies a legacy org-chart rule (e.g., routes certain dollar amounts to a VP)
  - Normalizes case or pads with leading zeros for an Oracle key
- **Response output**: Confirmation page or error redisplay.

## 3. Control Flow / Data Flow Walkthrough

1. Web server (likely Apache with `mod_cgi`) invokes the script per request.
2. Script prints `Content-Type` header, then reads CGI parameters.
3. If no form data present → render blank HTML form and exit.
4. If form submitted → validate fields (amount, date, category, approver).
5. Calls `mangle_approver($raw_input)` to normalize the approver value.
6. Opens DBI handle to Oracle: `DBI->connect("dbi:Oracle:SID", $user, $pass)`.
7. Prepares and executes INSERT (possibly with string interpolation rather than bind variables).
8. On success, prints confirmation HTML with the new expense ID.
9. Disconnects and exits.

## 4. Suspicious or Outdated Patterns

- **SQL injection risk**: 2003-era scripts often interpolate variables into SQL instead of using `?` placeholders with `bind_values`.
- **Hardcoded DB credentials**: Likely in plaintext at the top of the script.
- **No `use strict; use warnings;`**: Common omission that masks typos and scoping bugs.
- **CGI model itself is deprecated**: `CGI.pm` was removed from Perl core in 5.22 (2015); modern Perl uses PSGI/Plack.
- **Inline HTML**: No templating layer (Template Toolkit, Mason) means XSS risk if user input is echoed unescaped.
- **Opaque `mangle_approver`**: Un-commented transformation logic is a business-rule landmine—changes to HR systems may have silently broken it.
- **No transaction handling**: Likely auto-commit, so partial failures could leave orphan rows.
- **No logging**: Errors probably go to Apache's `error_log` via `die`, if at all.

## 5. Questions to Ask the Original Author (or Successor Team)

1. What exactly does `mangle_approver` produce—is it an LDAP UID, an Oracle FK, or something else?
2. Are there approver-routing rules based on dollar thresholds or department?
3. Where do the DB credentials come from, and who rotates them?
4. Is this still the system of record, or has data migrated elsewhere?
5. Are there known edge cases (contractors, terminated managers) that `mangle_approver` handles specially?
6. What downstream systems consume these rows—payroll, GL, audit?
Judge notes: Excellently structured, directly addresses the query intent with actionable insights into legacy code analysis, minor deduction only for not showing an actual code example walkthrough despite the strong framework provided.
Claude Haiku 4.5 9/10
# Legacy Perl Expense Report System – Reverse Engineering Analysis

## High-Level Purpose
This is a web-based expense report submission system built as a Perl CGI script that accepts user-submitted expense data, validates it, and stores it in an Oracle database for approval workflows. It serves as the bridge between frontend forms and the company's financial database.

## Key Components & Responsibilities

- **CGI Input Handler**: Parses form submissions from the web browser (employee ID, expense amounts, categories, etc.)
- **DBI Oracle Interface**: Executes SQL queries to read/write expense records and employee data
- **HTML Generator**: Dynamically builds web pages returned to the browser (no templating engine used)
- **`mangle_approver()` Subroutine**: Unknown transformation logic applied to approval chains—likely encrypts, obfuscates, or formats approver identifiers before database storage or email routing
- **Form Validation Logic**: Checks required fields and data types before database commit

## Control Flow / Data Flow Walkthrough

1. User submits expense form via HTTP POST
2. CGI script receives parameters (name, amount, date, manager ID, etc.)
3. Basic validation occurs in-memory (field presence, numeric checks)
4. `mangle_approver()` is called on the manager/approver identifier—output is stored or used in routing
5. SQL INSERT/UPDATE executes against Oracle, storing the expense record
6. HTML response is generated and returned to browser confirming submission
7. Downstream process (cron job or application) likely reads these records and routes approvals

## Suspicious or Outdated Patterns

- **CGI.pm usage**: CGI scripts are vulnerable to header injection and path traversal; modern frameworks (Catalyst, Dancer, Mojolicious) are standard
- **Inline HTML**: String concatenation to build HTML invites XSS vulnerabilities and is unmaintainable
- **No visible input sanitization**: SQL injection risk if `mangle_approver()` or other routines don't parameterize queries (check for `$dbh->prepare()` with placeholders vs. string concatenation)
- **No error handling**: Missing try/catch or die/warn statements likely mask database connection failures
- **Hardcoded credentials**: Oracle connection string may contain passwords in plaintext
- **No CSRF protection**: 2003-era script predates CSRF token patterns
- **No logging/audit trail**: Expense modifications are not tracked for compliance/fraud detection

## Questions to Ask the Original Author (or Reconstruct From Context)

1. **What does `mangle_approver()` actually do?** Does it hash, encrypt, format hierarchical chains, or look up manager escalations?
2. **Are all SQL queries parameterized?** Search for `$dbh->do()` or string interpolation in queries—these are critical vulnerabilities.
3. **How are approvers determined?** Is there a manager chain lookup, or is the approver hardcoded/user-supplied?
4. **What happens after insertion?** Does a separate approval workflow system consume these records?
5. **Are there any downstream dependencies** on the database schema or the specific format of data written by this script?

---

**Recommendation**: Before modifying this code, audit all SQL statements for injection vulnerabilities and document `mangle_approver()` by tracing its output into the database.
Judge notes: Excellently structured analysis that directly addresses the query intent with a comprehensive reverse-engineering framework, practical security insights, and actionable questions—minor deduction only because it doesn't include a code snippet example, though the output remains highly useful for understanding legacy codebases.
GPT-5.4 9/10
## 1) High-Level Purpose

This script is a classic early-2000s internal web app endpoint: it receives an expense report form submission through CGI (Common Gateway Interface, an older way to run web code), validates or reshapes the input, writes data into Oracle through DBI (Perl’s database interface), and returns an HTML response directly to the browser. It was likely built quickly as a self-contained intranet tool, combining request handling, business rules, database access, and presentation in one file because that was common for small internal systems at the time.

## 2) Key Components & Responsibilities

- **CGI request handling**
  - Reads submitted form fields from the web request.
  - Likely distinguishes between “new report,” “update,” or “submit for approval” based on parameters.

- **Input parsing / normalization**
  - Cleans up dates, amounts, employee IDs, cost centers, or descriptions.
  - May convert browser form values into the format Oracle expects.

- **`mangle_approver` subroutine**
  - Probably transforms an approver identifier into the final value stored or used.
  - Common possibilities: mapping username to employee ID, rewriting aliases, applying approval-routing rules, or sanitizing malformed input.

- **DBI + Oracle access**
  - Opens a database connection.
  - Runs `INSERT`, `UPDATE`, or `SELECT` statements for expense header/detail rows and approval metadata.

- **Inline HTML generation**
  - Produces success/error pages directly in Perl with embedded strings.
  - Likely echoes submitted values and displays the resulting report status.

- **Error handling**
  - Probably uses simple `die`, printed error messages, or conditional checks.
  - May expose raw database errors to the user.

## 3) Control Flow / Data Flow Walkthrough

1. **Web server invokes the CGI script** when the user submits the expense report form.
2. **Script reads CGI parameters** such as employee, date, amount, purpose, approver, and line items.
3. **Basic validation occurs**: required fields, numeric totals, date formats, maybe duplicate-checking.
4. **`mangle_approver` runs** on the approver field.
   - Most likely to normalize who the approver should be before saving or routing.
5. **Script connects to Oracle via DBI** using a DSN (data source name), username, and password.
6. **Business data is written/read**:
   - Insert/update report header.
   - Insert expense items.
   - Possibly lookup employee/manager info or approval chain.
7. **Outcome is determined**:
   - Success: report ID generated, status set, confirmation page rendered.
   - Failure: DB or validation error displayed in HTML.
8. **Script prints HTML response** directly and exits.

## 4) Suspicious or Outdated Patterns

- **Perl CGI architecture**: outdated, hard to maintain, and slower than persistent app frameworks.
- **Inline HTML mixed with logic**: makes changes risky and obscures business rules.
- **No comments/docs**: raises risk of hidden assumptions, especially around approvals and finance logic.
- **Possible SQL injection risk** if SQL is built with string concatenation instead of placeholders.
- **Possible weak input validation/escaping**: could allow malformed data or internal XSS (browser script injection).
- **Hard-coded DB credentials or SQL**: common in legacy CGI and a security/ops risk.
- **`mangle_approver` ambiguity**: “mangle” suggests ad hoc data rewriting rather than clear business logic.
- **Likely weak transaction handling**: partial writes could leave header/details out of sync.

## 5) Questions to Ask the Original Author

- What exactly is `mangle_approver` supposed to do, and what business rule does it encode?
- Are approvers users, managers, cost-center owners, or Oracle IDs?
- What are the required validation rules for amounts, receipts, dates, and duplicate submissions?
- Should the database writes be atomic (all succeed or all fail in one transaction)?
- What Oracle tables and stored procedures does this script depend on?
- Are there edge cases for delegates, temporary approvers, or out-of-office routing?
- What errors should users see versus what should only go to logs?
- Is this still the source of truth, or does another system also modify these expense records?
Judge notes: Excellent response that directly satisfies the query intent with a well-structured, practical AI-powered legacy code explanation that includes all requested sections, actionable insights, and realistic examples tailored to the Perl CGI expense system scenario.
Gemini 2.5 Pro 8/10
Of course. Here is a reverse-engineering analysis of the legacy Perl CGI script.

### 1. High-Level Purpose
This script is a classic, self-contained web application that serves two functions. First, it generates a web form for employees to enter expense report data. Second, it processes the data submitted through that form, applies some business logic to determine the correct approver, and then saves the final record into an Oracle database.

### 2. Key Components & Responsibilities
*   **CGI (Common Gateway Interface) Script:** The core of the application. This is a program the web server executes to dynamically generate a web page. It's responsible for handling both the initial display of the form and the subsequent processing of submitted data.
*   **Inline HTML Generation:** The script contains raw HTML code within the Perl logic itself, likely using `print` statements. This component is responsible for creating the user interface (the form and confirmation/error pages) that is sent to the user's browser.
*   **DBI (DataBase Interface) Module:** This is the data access layer. It's a standard Perl library used to connect to the Oracle database, prepare, and
Judge notes: The output directly addresses the query intent with a well-structured, technically sound explanation of legacy code analysis; minor truncation at the end slightly reduces completeness, but the framework and content delivered are highly useful and match the desired output type.
Grok 4.1 Fast Reasoning 9/10
### (1) High-Level Purpose
This Perl CGI script is a web form handler for submitting internal expense reports in 2003-era tech. It parses user-submitted form data (e.g., expenses, amounts, approvers), validates and stores it in an Oracle database using DBI (Database Interface module), and generates dynamic HTML responses like success pages or error forms. The mysterious `mangle_approver` subroutine likely custom-transforms approver data for workflow routing.

### (2) Key Components & Responsibilities
- **CGI parsing**: Uses CGI.pm to read POST/GET form fields (e.g., employee ID, expense details).
- **DBI/Oracle layer**: Connects to Oracle DB, executes INSERT/UPDATE/SELECT queries for reports and approvals.
- **HTML generation**: Inline `print` statements mix Perl logic with raw HTML for pages (forms, confirmations).
- **`mangle_approver` subroutine**: Processes approver fields—possibly maps IDs to emails, hierarchies, or obfuscates for security/privacy.
- **Validation/error handling**: Basic checks on inputs before DB ops, with redirects or HTML errors.

### (3) Control Flow / Data Flow Walkthrough
1. Script starts; `use CGI;` initializes, parses params into a hash (e.g., `$cgi->param('expense_amount')`).
2. DB connect: `$dbh = DBI->connect(dsn, user, pass)`; prepares statements.
3. Validate inputs (if/else on amounts, dates); if invalid, print HTML error form and exit.
4. Insert expense data via `$sth->execute(@bind_values)` (placeholders prevent SQL injection?).
5. Call `mangle_approver($approver_id)`—transforms output (e.g., lookup chain to final approver).
6. Update approval table with mangled data.
7. Print HTML success page (table of submitted report) or redirect; `$dbh->disconnect()`.

### (4) Suspicious or Outdated Patterns
- **Inline HTML**: Spaghetti code; prone to XSS (cross-site scripting) without escaping (`CGI::escapeHTML` likely missing).
- **Monolithic 300 lines**: No modularity; hard to test/maintain (anti-pattern: god script).
- **CGI in 2003**: Deprecated for modern frameworks (e.g., no CSRF tokens, session mgmt via cookies only).
- **DBI without placeholders?**: Risky SQL injection if concatenated queries.
- **`mangle_approver`**: Opaque name hints at business hack (e.g., hardcoded org chart mangling); potential data leak.
- **Hardcoded DB creds**: In script = security hole.

### (5) Questions to Ask the Original Author
- What exactly does `mangle_approver` compute (e.g., approver lookup logic, edge cases)?
- Any implicit business rules for expense validation or approval chains?
- Were DB credentials ever rotated; any encryption used?
- Known bugs or unhandled workflows (e.g., multi-level approvals)?

(Word count: 378)
Judge notes: Excellently structured, directly addresses the query intent with a realistic analysis of legacy Perl/CGI code, identifies genuine risks and anti-patterns, stays within word limit, and provides immediately actionable insights for understanding the codebase.

What makes these work

  1. 01
    Give the model a role and a goal

    Starting your prompt with 'You are a senior engineer reviewing legacy code for a modernization project' produces more targeted explanations than a bare code paste. The role anchors the model's perspective. Adding a specific goal, like 'flag security risks' or 'explain the business logic', stops the model from writing a generic summary and focuses output on what you actually need.

  2. 02
    Paste the smallest self-contained unit

    Pasting an entire 2,000-line file wastes context and dilutes the explanation. Isolate the function, class, or loop you need to understand and paste only that. If the function calls other functions you don't understand, follow up with those in separate prompts rather than dumping everything at once.

  3. 03
    Ask for risks and assumptions explicitly

    AI models will describe what code does but often omit what it assumes or breaks under. Adding 'also list any hidden assumptions and risks' to your prompt reliably surfaces hardcoded values, missing error handling, and edge cases the original author never documented. This is often the most valuable part of the explanation for legacy work.

  4. 04
    Iterate from summary to detail

    Start with a high-level prompt: 'In two sentences, what does this function do?' Once you confirm the model has the right mental model, follow up with 'Now explain each block in detail.' This two-pass approach catches model hallucinations early, before you've read a 500-word explanation built on a wrong assumption about the code's purpose.

More example scenarios

#01 · COBOL payroll batch job at a bank
Input
Explain this COBOL paragraph in plain English. Focus on what business logic it implements, what inputs it reads, and any risks or assumptions baked in:

CALC-OVERTIME.
    IF WS-HOURS-WORKED > 40
        COMPUTE WS-OT-PAY = (WS-HOURS-WORKED - 40) * WS-HOURLY-RATE * 1.5
    ELSE
        MOVE ZERO TO WS-OT-PAY
    END-IF.
Expected output
This paragraph calculates overtime pay. If an employee worked more than 40 hours, it multiplies the excess hours by 1.5 times their hourly rate. Otherwise it sets overtime pay to zero. Risk: the 1.5 multiplier is hardcoded, so any regulatory or policy change to the overtime rate requires a code change rather than a config update.
#02 · Legacy PHP e-commerce session handler
Input
I'm maintaining a PHP 5.4 e-commerce site. Explain what this function does, whether it has security issues, and what a modern replacement would look like:

function get_user_cart($uid) {
    $res = mysql_query("SELECT * FROM carts WHERE user_id='$uid'");
    return mysql_fetch_assoc($res);
}
Expected output
This function fetches a user's cart from the database. It has a critical SQL injection vulnerability: the $uid value is inserted directly into the query string without sanitization. An attacker can manipulate the input to read or delete arbitrary data. Replace it with a PDO prepared statement and parameterized query to close the vulnerability.
#03 · Undocumented Python ETL pipeline at a logistics company
Input
Explain this Python function step by step. What data transformation is it performing and what could go wrong at scale?

def normalize_shipments(df):
    df = df.dropna(subset=['origin', 'dest'])
    df['route'] = df['origin'].str.upper() + '-' + df['dest'].str.upper()
    df['weight_kg'] = df['weight_lbs'] * 0.453592
    return df.drop_duplicates(subset=['shipment_id'])
Expected output
This function cleans and standardizes a shipments dataframe. It drops rows missing an origin or destination, creates a standardized route string in uppercase, converts weight from pounds to kilograms, and removes duplicate shipment IDs. At scale, the silent row drops on missing origin/dest could hide data quality issues upstream that should be logged or alerted on instead.
#04 · Fortran scientific simulation module
Input
This Fortran 77 subroutine is used in a climate model. Explain what it calculates and flag any numerical precision concerns:

SUBROUTINE CALCTMP(T, P, N)
REAL T(N), P(N)
DO 10 I = 1, N
  T(I) = T(I) + (P(I) * 0.00001)
10 CONTINUE
RETURN
END
Expected output
This subroutine adjusts a temperature array by adding a pressure-scaled increment to each element. The 0.00001 factor suggests a unit conversion or physical constant, but it is hardcoded with no comment explaining its origin. In single-precision Fortran 77, repeated addition of small floating-point values across large arrays can accumulate significant rounding error, which is a known risk in long-running climate simulations.
#05 · VBA macro in a finance team's Excel workbook
Input
Explain what this Excel VBA macro does and whether it is safe to run on a shared network drive:

Sub UpdateRates()
    Dim ws As Worksheet
    Set ws = ThisWorkbook.Sheets("Rates")
    ws.Range("B2:B50").Value = ws.Range("C2:C50").Value
    ThisWorkbook.Save
End Sub
Expected output
This macro copies values from column C into column B on the Rates sheet, then immediately saves the workbook. It overwrites whatever was in column B with no confirmation prompt and no backup. On a shared network drive, if two users trigger it simultaneously, one user's save will silently overwrite the other's, risking data loss. Add a backup step or move to a database for concurrent access.

Common mistakes to avoid

  • Trusting the output without verification

    AI explanations are plausible, not guaranteed correct. A model can confidently explain that a function sorts a list ascending when it actually sorts descending because of an off-by-one in a comparator. Always validate the explanation against the actual behavior, either by running the code or by tracing through it manually after the AI gives you a hypothesis.

  • Pasting code with missing context

    If your function references global variables, external constants, or class state that you didn't include in the paste, the model will invent plausible-sounding values for them. The explanation sounds complete but is partially fabricated. Include any constants, class definitions, or relevant global state that the function directly uses.

  • Skipping the language and version

    COBOL, Fortran, and older versions of PHP or Python have behaviors that differ meaningfully from modern equivalents. If you don't specify the language version, the model may explain code as if it runs in a modern runtime, missing version-specific quirks like PHP 5 mysql_ functions or Python 2 integer division. Always specify the language and version in your prompt.

  • Using explanations as official documentation

    AI-generated explanations are a starting point for understanding, not a finished artifact. Treating the output as authoritative documentation in a regulated or safety-critical system is a compliance and liability risk. Use AI to build your understanding, then write the documentation yourself with a human review step.

Related queries

Frequently asked questions

Which AI model is best for understanding legacy code?

Claude 3.5 Sonnet and GPT-4o consistently outperform smaller models on legacy code comprehension because they handle long, dense code blocks and older syntax better. For COBOL or Fortran specifically, Claude tends to produce more historically accurate explanations. The comparison table on this page shows head-to-head output quality across four models on the same prompt so you can judge for your specific language.

Can AI explain code in languages like COBOL or Fortran?

Yes, frontier models have been trained on enough legacy language examples to produce useful explanations of COBOL, Fortran, BASIC, and older versions of common languages. Quality drops for obscure dialects or heavily customized mainframe syntax. Providing the language name and any dialect details in your prompt improves accuracy significantly.

Is it safe to paste proprietary code into ChatGPT or Claude?

That depends on your company's data policy and the API terms you're operating under. The ChatGPT API with data privacy settings disabled and Claude's API via Anthropic do not use your inputs for training by default, but you should verify current terms and get clearance from your legal or security team before pasting code that contains trade secrets, PII, or regulated data. Many enterprises use self-hosted or enterprise-tier deployments specifically to avoid this risk.

How do I use AI to document an entire legacy codebase, not just one function?

Work module by module rather than trying to process the whole codebase at once. Start with entry points, public interfaces, and the functions touched most frequently by bugs or change requests. Tools like GitHub Copilot Chat, Cursor, and Sourcegraph Cody are built to work across a full repo with file-level context, which gives better results than pasting isolated snippets into a chat interface for large-scale documentation projects.

Can AI help me understand legacy code before a migration project?

This is one of the strongest use cases. Use AI to generate plain-English descriptions of each module's behavior, then use those descriptions to write your migration specification. The AI explanation tells you what the system does; your engineers decide how to rebuild it. This approach is faster than manual code archaeology and produces a readable spec that non-engineers on the project can review.

What should I do when the AI explanation contradicts what the code actually does?

Treat it as a signal that the code has a non-obvious behavior worth investigating. Re-run the prompt with more context, including any global state or calling code the function depends on. If the explanation is still wrong after providing full context, add 'trace through this code step by step with example inputs' to your prompt. Step-by-step traces are harder to hallucinate than summary explanations.

Try it with a real tool

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