Part 2: AI Terms Explained | Temperature, Context & Tool Calling

Welcome back to the channel! In Part 2 of our AI series, we continue diving into the essential terms you need to know to understand Large Language Models (LLMs) In our previous tutorial, we covered the difference between AI agents and workflows, as well as tokens In this video, we explore: Temperature: Understand how this value controls the "creativity" of an LLM's response, from generic answers at 0 to more varied responses at higher levels Context & Context Size: We look at how models like DeepSeek and Grok use "context" as a memory space for additional information, and why staying within the token limit is vital to prevent false information. Hallucination: Discover why AI models sometimes provide wrong answers with extreme confidence and how factors like context size and prompt framing contribute to this Tool Calling (Function Calling): This is the key concept that turns a "dumb" AI into a powerful "AI Agent." Learn how tools allow AI to access real-time data, like the current time or internet searches, rather than relying solely on historical training data Timestamps: 0:00 - Recap of Part 1 (Tokens & Workflows) 0:45 - Temperature: Controlling AI Creativity 3:30 - What is Context? (Feeding Info to LLMs) 6:15 - Comparing Context Sizes: DeepSeek vs. Grok 9:40 - Why AI Hallucinates (Confident Mistakes) 12:50 - Tool Calling: The Secret to AI Agents 17:00 - Summary & What’s Coming in Part 3 Resources Mentioned: Open Router (for testing various models) DeepSeek Grok If you missed Part 1, make sure to check it out on the channel. Don't forget to like, subscribe, and leave your questions below! #AITerms #LLM #AIAgents #DeepSeek #ToolCalling #GenerativeAI #VimalMenon