Mastering AI Driven Development - Lesson 5

⚠️ RECORDED: February 2025 Models and features evolve quickly, but these core techniques remain my daily workflow. Join my community for latest updates: https://www.skool.com/prompt-to-prod-9369 GitHub Repository for this lesson: https://github.com/jayminwest/tf-ai-development-lesson05 Lesson 5 - Architect Mode in Aider 📖 Project Overview Welcome to Lesson 5! We introduce Architect Mode in Aider, where two AI models collaborate in software development. You'll learn: - Install and configure Ollama for local language models - Use Architect Mode to break down complex tasks - Generate structured prompts for architect-driven development - Implement a Flask-based Wikipedia summarization tool 📌 Lesson Breakdown 1️⃣ Setting Up the Project (00:00:00:02 - 00:00:43:15) - Clone the GitHub repository: git clone [repo_link] - Install Ollama from https://ollama.com - Ollama enables running LLMs locally for improved AI development 2️⃣ Understanding Architect Mode (00:00:47:03 - 00:02:22:01) - Two LLMs collaborate: - Architect Model: Plans and structures development - Editor Model: Implements the planned changes - Benefits: Large-scale modifications, consistency, structured development - Prompting structure: - High-level goals (overall objective) - Mid-level goals (specific components) - Implementation guidelines (standards, dependencies) - Low-level goals (specific functions) 3️⃣ Writing Effective Architect Prompts (00:02:22:01 - 00:03:12:12) Example Structure: ## High-Level Goal - Build Flask web app for Wikipedia articles with Ollama summarization ## Mid-Level Goals - Implement Flask server - Create user input form - Fetch Wikipedia articles - Integrate Ollama for summarization ## Implementation Guidelines - Use Flask, Bootstrap, JavaScript - Ensure async API handling - Maintain error handling ## Low-Level Goals - Create app.py with routes - Implement fetch_article() function - Create index.html with UI 4️⃣ Running Architect Mode (00:03:12:12 - 00:04:02:01) - Launch Aider: aider --model a1-preview --editor-model claude-3.5-sonnet - Execute prompt: /architect [paste your structured prompt] - Workflow: Architect plans → Editor implements → AI refines 5️⃣ Implementing Wikipedia Tool (00:04:02:01 - 00:06:12:24) Flask setup: from flask import Flask, render_template, request from wikipedia_fetcher import fetch_article app = Flask(__name__) @app.route('/', methods=['GET', 'POST']) def index(): if request.method == 'POST': article_name = request.form['article_name'] article_text = fetch_article(article_name) return render_template('index.html', article=article_text) return render_template('index.html') if __name__ == '__main__': app.run(debug=True) Run server: python app.py Test at: http://127.0.0.1:5000/ 6️⃣ Troubleshooting (00:06:12:24 - 00:08:02:12) - Ollama API Errors: Ensure Ollama running with: ollama serve - Flask Issues: Restart with: python app.py - Missing files: Re-add with: /add [filename] 7️⃣ Next Steps (00:08:02:12 - 00:09:12:01) - Practice modifying the Flask app - Add more features, improve error handling - Lesson 6: Advanced multi-model workflows 🎯 Action Item: Build your own AI summarization tool using Architect Mode! 🚀 Conclusion You now understand: ✅ Setting up Architect Mode ✅ Structuring effective prompts ✅ Implementing Flask-based tools ✅ Troubleshooting AI-generated code See you in Lesson 6! 🎯