In this lesson, you’ll learn how to leverage AI (via Composer) to debug a Flask application. We’ll capture runtime errors in the browser or terminal, feed them into Composer alongside the relevant code, and apply the suggested fixes—all while applying your own programmer intuition.Documentation Index
Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt
Use this file to discover all available pages before exploring further.
1. Reproducing & Resolving a Jinja2 Template Error
- Start your Flask development server and navigate to the login page.
- Log in with valid credentials.

- After logging in, you may encounter:

The Faulty Route
In app.py, the dashboard route is defined as:Feeding the Error and Code to Composer
“Help me debug this error.”Composer identifies:
Issue: You referencedash.html, but your templates directory containsdashboard.html.
Solution: Rename the template or update the call torender_template('dashboard.html', …).
Always verify your
templates/ folder matches the filenames you pass to render_template().Applying the Fix
2. Debugging a Database Column Typo
Next, attempt to create a new task via the web interface:
The Faulty INSERT Statement
In app.py, the handler is:Feeding Code and Error to Composer
Cause: The column namestatu3is mistyped. It should bestatus.
Fix: Correct the column name in the SQL statement.
Double-check your database schema (e.g.,
schema.sql) before running migrations or inserts.Applying the Fix
3. Best Practices for AI-Assisted Debugging
| Error Type | Common Cause | Recommended Fix |
|---|---|---|
| Jinja2 TemplateNotFound | Filename mismatch | Update render_template() to the correct name |
| sqlite3.OperationalError | Column name typo | Correct the column in your SQL statement |
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Capture Complete Context
- Include full tracebacks or error messages.
- Paste only the relevant code where the error occurred.
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Provide Schema or Definitions
- Share your
schema.sqlor data models so the AI can verify table and column names.
- Share your
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Trust but Verify
- Apply your own programming knowledge to confirm suggested changes align with your codebase.
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Experiment with Multiple Models
- If Composer’s recommendation isn’t clear, try another model (e.g., GPT-3.5, GPT-4, Gemini) or start a fresh session.
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Use Logs as Supplemental Context
- Include application or server logs (from SSH sessions or production environments) for deeper insights.