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Yash Karanke
Yash Karanke

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How I Build Context ?

#ai #codex #coding #promptengineering

Hey folks, if you've ever asked an AI for code and gotten back something that works... but barely, you're not alone. I've been there—staring at a snippet that's missing error handling or scalability thoughts, wondering why I bothered. As a dev with years under my belt, I realized the fix isn't better models; it's better prompts. Specifically, crafting context that makes the AI think like a senior engineer.
Enter my "mega prompt"—a simple template that packs a punch. It sets the AI's role, structures the output, and bakes in quality checks. No more vague responses; just solid, deployable work.

The Prompt That Changed Everything

Here's the core of it (tweak as needed):

# ROLE
You are a senior software engineer with 15+ years of production experience across full-stack development, system design, and DevOps.

# TASK BREAKDOWN
For every coding request, structure your response as:
1. Architecture & Design Decisions - explain the approach and why
2. Implementation - write complete, production-ready code
3. Edge Cases - identify potential failures and handle them
4. Testing Strategy - unit tests and integration considerations
5. Deployment Notes - what to watch in production

# CODE QUALITY STANDARDS
- Include error handling and logging
- Add inline comments for complex logic
- Follow language-specific best practices
- Optimize for readability first, performance second
- Provide security considerations where relevant

# OUTPUT FORMAT
Present code in executable blocks. Explain tradeoffs between different approaches. If something will break at scale, tell me now.
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A Quick Win: Login Endpoint Example

Say I need a secure Flask login with JWT. I paste the prompt, add my task, and boom—structured gold. Highlights from a recent run:

Architecture: Stateless JWT for scalability; bcrypt for hashing to keep things secure without overkill.

  • Code Snippet (key parts): 
from flask import Flask, request, jsonify
from flask_jwt_extended import create_access_token
import bcrypt
import logging

app = Flask(__name__)
# ... config ...

@app.route('/login', methods=['POST'])
def login():
    try:
        data = request.get_json()
        username, password = data.get('username'), data.get('password')

        if not username or not password:
            return jsonify({'error': 'Missing credentials'}), 400

        # Verify user and password (mock DB here)
        if username not in USERS_DB or not bcrypt.checkpw(password.encode(), stored_hash):
            return jsonify({'error': 'Invalid credentials'}), 401

        token = create_access_token(identity=username)
        return jsonify({'access_token': token}), 200
    except Exception as e:
        logging.error(f"Login failed: {e}")
        return jsonify({'error': 'Server error'}), 500
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  • Edges & Tests: Handles bad inputs gracefully; suggests pytest units for verification.
  • Deploy Tip: Watch CPU on bcrypt at scale—swap to Argon2 if needed. This took minutes, not hours, and caught issues I'd have missed.

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