The Art of the Ask: A Compendium of Advanced Codex Prompt Examples Unlocking the Full Potential of Your AI Engineering Partner – June 2025

In my Python Flask application, I'm getting a 'KeyError: "user_id"' in `app/routes.py` within the `get_user_profile` function specifically when a new user who hasn't completed onboarding tries to access their profile. Scan `app/models.py` for the User model structure and `app/auth.py` for the session handling. Identify the root cause and apply a fix to `routes.py` that gracefully handles this case by redirecting to the onboarding page (`/onboard`) if `user_id` is missing or the profile is incomplete. Ensure the fix includes appropriate logging for this event.

The June 2025 Codex, with its repository-wide understanding, would first parse the specified files (`routes.py`, `models.py`, `auth.py`). It would analyze the `get_user_profile` function, the `User` model structure (looking for fields that indicate profile completeness or onboarding status), and how user sessions or `user_id` are managed in the auth logic.

Codex would likely identify that the `KeyError` arises from attempting to access a dictionary key or session variable that hasn't been set for users in that specific state. It might then propose a change to `routes.py` similar to:

• Adding a check at the beginning of `get_user_profile` to see if `user_id` is in the session or if a related user profile field (e.g., `is_onboarded`) is false.
• If the condition is met, it would insert a `redirect(url_for('onboard'))` call (assuming Flask's `url_for` and a route named 'onboard').
• It would also add a logging statement (e.g., `current_app.logger.info('User redirected to onboarding due to incomplete profile.')`) before the redirect.

Codex might present the exact code changes as a diff and explain its reasoning. It might also ask for clarification if the "profile completeness" logic is ambiguous across the provided files.
Benefits: Rapid root cause analysis and fix implementation, consistent error handling, and automated logging, saving significant debugging time.

Analyze all public functions and classes within the 'src/core/utils/' directory of my project. For each, generate comprehensive docstrings following the Google Python Style Guide. Ensure each docstring includes:
1. A concise summary of the function/class purpose.
2. Detailed descriptions for all arguments/parameters, including their expected types.
3. A clear description of the return value and its type.
4. Any exceptions that the function/class might explicitly raise, along with conditions for raising them.
Update the files in place.

Codex would first scan all Python files in the specified directory. For each public function and class, it would analyze its signature (parameters, name) and, importantly, attempt to infer its behavior and purpose by examining its internal logic and how it's used elsewhere in the codebase (if that context is available or if Codex has indexed the repo).

It would then generate docstrings formatted according to the Google Python Style Guide. For a function like `def calculate_mean(numbers: list[float]) -> float:`, it might produce: ```python """Calculates the arithmetic mean of a list of numbers. Args: numbers (list[float]): A list of floating-point numbers. An empty list will raise a ValueError. Returns: float: The arithmetic mean of the numbers in the list. Raises: ValueError: If the input list 'numbers' is empty. """ ``` Codex would systematically go through each item, inferring types if not explicitly hinted (though type hints in the original code greatly improve accuracy) and attempting to identify common exceptions like `ValueError` or `TypeError` based on internal checks. It would then present the modified files or a diff for review before changes are committed.
Benefits: Saves massive amounts of developer time, ensures consistent documentation style, improves code readability and maintainability, and facilitates easier onboarding for new team members.

This project is a monolithic Django 2.2 application running on Python 3.7. I need to migrate it to Django 4.2 and Python 3.11.
Tasks:
1. Analyze `requirements.txt` and update all dependencies to versions compatible with Django 4.2/Python 3.11, outputting a new `requirements_updated.txt`. Pay special attention to Django, DRF, Celery, and database connectors.
2. Scan the entire codebase for deprecated Django API usage (e.g., `url()` vs `path()`, `ugettext` vs `gettext`, changes in `HttpRequest`, `HttpResponse`, ORM, and middleware). Refactor the code to use the modern APIs.
3. Identify and update any Python 3.7 specific idioms or deprecated modules to their Python 3.11 equivalents.
4. Provide a detailed summary report of all major changes made, potential breaking changes that require manual review, and a list of files modified.
Do not execute tests, but flag areas that will likely need test updates.

This is a complex task where the June 2025 Codex would shine by demonstrating its "AI Engineering Partner" capabilities.
Phase 1 (Analysis & Planning): Codex would first parse `requirements.txt`. It would cross-reference each package with compatibility information for Django 4.2 and Python 3.11 (leveraging its vast training data which includes knowledge of package ecosystems and changelogs). It would then generate `requirements_updated.txt`. It would also scan the entire codebase, building a dependency graph and identifying usages of known deprecated Django and Python patterns based on its knowledge of version changes.

Phase 2 (Code Refactoring): Codex would systematically refactor the identified code sections. For example, it would replace `url()` imports and usage with `path()` or `re_path()`, update deprecated middleware class structures, adjust ORM query patterns if needed, and handle changes in request/response object attributes.

Phase 3 (Reporting): Finally, Codex would generate a comprehensive Markdown report detailing:

• A summary of the version changes for key dependencies.
• A categorized list of API changes addressed (e.g., URL patterns, internationalization, ORM).
• Specific examples of significant refactorings.
• A list of all modified files.
• A section on "Potential Manual Review Items," highlighting areas where the automated refactoring might be ambiguous or where related business logic might need re-validation (e.g., complex custom template tags, highly intricate database queries, or areas it flagged for needing test updates).

Codex would likely present the changes as a series of proposed commits or a large diff for the developer to review and then apply incrementally or all at once. It might ask clarifying questions during the process if it encounters highly ambiguous or project-specific patterns it cannot confidently refactor.
Benefits: Dramatically accelerates a complex and risky migration process, reduces manual error, provides a clear audit trail of changes, and helps identify areas requiring focused human attention.

Review the changes in pull request #248 (feature-branch: 'new-user-feedback-module').
Focus on:
1. Potential security vulnerabilities: Specifically check for XSS in template rendering related to user feedback display, SQL injection possibilities in any new database interactions, and any obvious insecure direct object reference (IDOR) patterns.
2. Logic errors or significant deviations from best practices in the new Python and JavaScript code.
3. Adherence to our project's coding standards (documented in `CONTRIBUTING.md` - section "Code Style").
4. Lack of appropriate error handling or missing unit tests for new functionality.
Provide a structured report with file paths, line numbers, issue descriptions, and actionable suggestions for improvement. Assign a severity (Critical, High, Medium, Low) to each identified issue.

Assuming Codex has access to the PR data (e.g., via GitHub integration or by being provided the diff and relevant project files, including `CONTRIBUTING.md`), it would perform a multi-faceted analysis:

• Security Analysis: It would scan for common XSS patterns where user-supplied feedback is rendered, check database query construction for SQLi risks (looking for proper parameterization or ORM usage), and analyze how object IDs are used in new API endpoints or views for IDOR.
• Logic & Best Practices: It would analyze the control flow, look for off-by-one errors, inefficient loops, unhandled exceptions, or use of deprecated/suboptimal library functions.
• Style Adherence: It would parse `CONTRIBUTING.md` to understand the project's specific style rules (e.g., variable naming, import order, line length) and flag deviations in the new code.
• Test & Error Handling Coverage: It would identify new functions or significant code paths and check if corresponding unit tests appear to be added or if error conditions seem unhandled.

The output would be a structured report, likely in Markdown, formatted for readability within a PR comment or a separate document:

## Codex PR Review: #248

### Critical Issues:
- **File:** `templates/feedback/view.html`, **Line:** 42
  - **Issue:** Potential XSS vulnerability. User-submitted `feedback_text` is rendered directly without sanitization.
  - **Suggestion:** Use Jinja2's `|e` filter or ensure proper sanitization library is applied.

### High Issues:
- **File:** `views/feedback_api.py`, **Line:** 78
  - **Issue:** SQL query constructed using string formatting with user input `category_id`. Potential SQLi.
  - **Suggestion:** Refactor to use parameterized queries or ORM filtering.

### Medium Issues:
- **File:** `static/js/feedback.js`, **Line:** 25
  - **Issue:** Missing error handling for AJAX request failure.
  - **Suggestion:** Add a `.catch()` block or equivalent to handle network errors or non-200 responses.
- **File:** `models/feedback.py`, **Line:** 15 (Class `FeedbackModel`)
  - **Issue:** Class name does not follow PascalCase as per `CONTRIBUTING.md`.
  - **Suggestion:** Rename to `FeedbackModel`.

### Low Issues:
- **File:** `views/feedback_views.py`, **Function:** `submit_feedback`
  - **Issue:** No unit test found for the new `submit_feedback` function.
  - **Suggestion:** Add unit tests covering successful submission and error cases.

Benefits: Catches a wide range of issues early, improves overall code quality and security, reduces the burden on human reviewers, and helps enforce consistency.

I'm new to the 'notification-service' module in our microservices architecture. It's written in Go. Please provide:
1. A high-level explanation of its primary responsibilities and how it interacts with other services (like 'user-service' and 'order-service').
2. A breakdown of the main components/packages within 'notification-service' and their roles.
3. An explanation of the error handling strategy and logging mechanisms used.
4. Generate a Mermaid sequence diagram illustrating the typical flow when a new order is placed and a notification needs to be sent to the user.
Assume you have access to the 'notification-service', 'user-service', and 'order-service' repositories.

Codex would leverage its multi-repository understanding to analyze the specified services.

Outcome Document (likely Markdown):
**1. High-Level Responsibilities & Interactions:** Codex would describe that the `notification-service` is responsible for dispatching various types of notifications (email, SMS, push) to users based on events from other services. It would identify API calls or message queue topics it subscribes to from `order-service` (e.g., "order_created") and how it might query `user-service` for contact details.

**2. Main Components Breakdown:** It might list packages like `internal/broker` (for Kafka/RabbitMQ connections), `internal/transports` (for email/SMS providers), `internal/templates` (for notification messages), and `internal/core` (for business logic), explaining each one's role.

**3. Error Handling & Logging:** Codex would identify common error handling patterns (e.g., custom error types, retry mechanisms for sending notifications) and how logging is implemented (e.g., structured logging with specific fields like `trace_id`, `user_id`).

**4. Mermaid Sequence Diagram:** It would generate a text-based diagram like this: ```mermaid sequenceDiagram participant Client participant OrderService as OS participant NotificationService as NS participant UserService as US Client->>+OS: Place Order OS->>OS: Process Order OS-->>-Client: Order Confirmation (ID) OS->>NS: Event: new_order (orderID, userID) NS->>+US: GetUserDetails(userID) US-->>-NS: UserDetails (email, phone) NS->>NS: Prepare Notification (template, userDetails) NS->>ExternalProvider: Send Email/SMS ExternalProvider-->>NS: Delivery Status NS->>NS: Log Notification Status ``` Benefits: Drastically reduces onboarding time, provides a clear and structured understanding of complex modules, helps in identifying points of failure or areas for improvement, and generates useful documentation artifacts.