Data Analytics & AI | SQL Interviews | Power BI Resources

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Python Libraries for Data Science

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Python Basics: A Practical Introduction to Python 3 📖 Book

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If you're building AI agents, you should get familiar with these 3 common agent/workflow patterns. Let's break it down. 🔹 Reflection You give the agent an input. The agent then "reflects" on its output, and based on feedback, improves and refines. Ideal tools to use: - Base model (e.g. GPT-4o) - Fine-tuned model (to give feedback) - n8n to set up the agent. 🔹 RAG-based You give the agent a task. The agent has the ability to query an external knowledge base to retrieve specific information needed. Ideal tools to use: - Vector Database (e.g. Pinecone). - UI-based RAG (Aidbase is the #1 tool). - API-based RAG (SourceSync is a new player on the market, highly promising). 🔹 AI Workflow This is a "traditional" automation workflow that uses AI to carry out subtasks as part of the flow. Ideal tools to use: - n8n to handle the workflow. - GPT-4o, Claude, or other models that can be accessed through API (basic HTTP requests). If you can master these 3 patterns well, you can solve a very broad range of different problems.