Function Calling vs Tool Calling Explained 🤖 | Video 20 #llm #ai #tools

🚀 *What if an LLM could do more than just generate text? What if it could actually take action?* Welcome to another video in my AI Engineering learning series! 🧠 LLMs are powerful at understanding and generating language, but real-world AI applications often need more than text generation. They need to call APIs, retrieve data, perform calculations, interact with databases, and execute tasks. That's where *Function Calling and Tool Calling* come in! 📚 *What You'll Learn:* ✅ What is Function Calling? ✅ What is Tool Calling? ✅ Function Calling vs. Tool Calling — what's the difference? ✅ How an LLM decides when to call a tool ✅ How tool schemas and function arguments work ✅ How the application executes a tool call ✅ How tool results are returned to the LLM ✅ Practical examples using APIs and external tools ✅ How tool calling powers AI agents and agentic workflows 🔄 *The Basic Workflow* User Prompt → LLM → Tool Call → Tool Execution → Tool Result → LLM → Final Response The key idea: *the LLM usually decides what tool to call, while your application or agent runtime executes the tool and returns the result.* 🎯 *Who Should Watch?* Aspiring AI and Applied AI Engineers Senior AI Engineers preparing for architecture-level responsibilities Forward Deployed Engineers (FDEs) Developers building AI agents and LLM-powered applications Data Scientists and ML Engineers transitioning into Generative AI 📺 *Explore my LLM Fundamentals playlist:* 👉    • LLM Fundamentals Explained 🤖 | Complete Ge...   💡 My goal is to help you move beyond simply calling LLM APIs and understand the concepts needed to build practical, reliable AI systems. 👍 Like, share, and subscribe if you're learning AI engineering! 💬 *Question:* If you were building an AI assistant, which tool would you connect first — a database, a web search API, or a calculator? Let me know in the comments! #FunctionCalling #ToolCalling #AIAgents #LLM #GenerativeAI #AIEngineering #AgenticAI #LLMFundamentals