# Using AI in Meta’s AI-assisted coding interview (with real prompts and examples)

By Githire B. Wahome \| Published: January 20, 2026; Last updated: April 20, 2026

Githire B. Wahome

Githire (Brian) Wahome is a backend and machine learning engineer with almost a decade of experience across startups and large technology companies. He’s worked at Meta, Microsoft, and Qualtrics.

In October 2025, Meta began piloting an AI-enabled coding interview that replaces one of the two coding rounds at the onsite stage. It’s 60 minutes in a specialized CoderPad environment with an AI assistant built in.

## Quick facts

- **Interview length:** 60 minutes
- **Where it fits in:** This round replaces one of the coding interviews in the onsite
- **Who gets it:** Likely rolled out to all SWE roles in 2026
- **Platform:** CoderPad with an integrated AI assistant
- **AI Models available:** GPT-4o mini, GPT-5, Claude Sonnet 4/4.5, Claude Haiku 3.5/4.5, Gemini 2.5 Pro, Llama 4 Maverick
- **Key difference (outside of AI use):** Multi-file project that you have to iterate on instead of two algorithmic problems

## Practical applications of AI during an interview

AI coding assistants in this round are best used as a productivity booster for well-defined subtasks, NOT as an end-to-end solver. Here are some concrete ways a candidate can deploy AI in a back-end interview.

### Shell commands and scripting

Shell commands and scripting are common in backend/ops roles and are a perfect example of a well-defined subtask well suited to AI.

#### Example

|     |     |
| --- | --- |
| **Scenario** | Suppose that during the interview you need to quickly find all ERROR entries in log files.<br> Instead of typing out the command from memory, you can prompt the AI. |
| **Prompt** | Write a bash command to recursively search for lines containing ERROR in all<br> `.log` files in the current directory and subdirectories. |
| **AI response** | `grep -r "ERROR" --include="*.log" .` |
| **Tell the interviewer** | "I've used AI to generate the grep command. The `-r` flag searches recursively,<br> `--include` filters to `.log` files only, and the dot specifies the<br> current directory. This saves time on syntax lookup." |

### Code comprehension and navigation

AI can summarize what unfamiliar code does or find references quickly, which is especially helpful when you're dropped into an existing codebase.

#### Examples

|     |     |
| --- | --- |
| **Scenario** | Suppose you're given a legacy Python function that processes user data, but you're not immediately sure what transformations it applies.<br>  You can paste the function into the AI and prompt it. |
| **Prompt** | Explain what this function does step-by-step and identify any potential edge cases or bugs. |
| **AI response** | This function takes a list of user dictionaries, filters out users without an `email` field,<br> normalizes email addresses to lowercase, and returns a sorted list by `username`.<br>**Potential issues:** it assumes `email` and `username` keys exist;<br> missing keys will raise `KeyError`.<br>**Edge case:** empty input list returns empty list correctly. |
| **Tell the interviewer** | "I used AI to quickly understand this legacy code. It confirmed my suspicion that the function filters and<br> normalizes emails. I see it doesn't handle missing keys gracefully, so I'll add error handling for that." |

## Boilerplate generation

You can also use AI to offload generating boilerplate or repetitive coding tasks. Boilerplate generation is especially valuable in backend/ops interviews where you need to quickly scaffold APIs, data models, or configuration files.

#### Examples

**Rest API endpoint**  
|     |     |  
| --- | --- |  
| **Scenario** | Suppose during the interview you need to create a REST API endpoint for user registration.<br> Please generate the necessary structure. |  
| **AI response** |  ```python
from flask import Flask, request, jsonify

app = Flask(__name__)
@app.route('/api/register', methods=['POST'])
def register_user():
    data = request.get_json()
    # Validate required fields
    if not data or not all(k in data for k in ['username', 'email', 'password']):
        return jsonify({'error': 'Missing required fields'}), 400
    # Additional implementation here...
    return jsonify({'message': 'User registered successfully'}), 201

if __name__ == '__main__':
    app.run(debug=True)
``` |  
| **Tell the interviewer** | “I've used AI to generate the Flask endpoint boilerplate. It includes basic validation for<br> username length, email format, and password strength." |

## Other uses

### Debugging assistance

Use AI as a pair-programming partner when debugging. If you encounter an error or unexpected behavior, you can describe the issue or even share an error message with the AI to get troubleshooting suggestions.

### Test case generation

Rapidly generate test cases or examples with AI. After writing a function, ask the AI to provide a comprehensive list of unit test cases. This approach can help you ensure comprehensive coverage.

### Solution optimization

Once you have a solution, you can query the AI for possible improvements. For example: _Can this function be optimized or made more Pythonic?_

## TL;DR: Best practices for effective AI-assisted coding

1. Fully understand the problem first.  
2. Use AI for subtasks, not the entire design.  
3. Provide clear, contextual prompts.  
4. Iterate in small, controlled steps.  
5. Review all AI outputs critically.  
6. Test thoroughly, and verify behavior.  
7. Take ownership of the solution.  
8. Communicate and justify your use of AI.  
9. Don’t over-rely on AI, and make sure you maintain your skills.  
10. Manage time and AI usage wisely.