1 00:00:03,300 --> 00:00:08,859 Welcome to this video on why AI needs tools from guessing to real-world action. 2 00:00:08,859 --> 00:00:13,319 In this video, you'll explore how tools help large language models, or LLMs, move 3 00:00:13,319 --> 00:00:16,879 beyond simple text generation and handle real-world tasks. 4 00:00:16,879 --> 00:00:21,559 You'll learn how LLMs evolve into intelligent agents with the help of tools. 5 00:00:21,559 --> 00:00:27,239 You'll also discover how tools improve accuracy and reliability in LLM responses. 6 00:00:27,239 --> 00:00:33,319 LLMs weren't originally designed to handle real-world tasks like complex math, API calls, 7 00:00:33,319 --> 00:00:35,279 or live data retrieval. 8 00:00:35,279 --> 00:00:38,400 But tools can equip them with these abilities. 9 00:00:38,400 --> 00:00:43,799 While LLMs are incredibly powerful at generating human-like text, they're far from perfect. 10 00:00:43,799 --> 00:00:45,000 Why? 11 00:00:45,000 --> 00:00:47,560 Because without the right tools, they're just guessing. 12 00:00:47,560 --> 00:00:51,200 At their core, LLMs are pattern recognition machines. 13 00:00:51,200 --> 00:00:55,439 They don't know real-time facts, can't access APIs, and can't interact with the 14 00:00:55,439 --> 00:00:56,439 world. 15 00:00:56,439 --> 00:01:01,159 It's like asking a super-smart person to solve a real-world problem, but blindfolded 16 00:01:01,159 --> 00:01:04,400 with no calculator, no Google, and no phone. 17 00:01:04,400 --> 00:01:09,120 Tools allow LLMs to interact with the real world, perform math, retrieve facts, and take 18 00:01:09,120 --> 00:01:11,720 actions they otherwise couldn't. 19 00:01:11,720 --> 00:01:13,319 Tools aren't just optional. 20 00:01:13,319 --> 00:01:18,239 They're essential for moving from text generation to problem-solving. 21 00:01:18,239 --> 00:01:21,879 Let's explore the powerful capabilities that tools provide to LLMs. 22 00:01:21,879 --> 00:01:26,279 First, they enable the retrieval and processing of information that isn't available in the 23 00:01:26,279 --> 00:01:27,839 LLM's training data. 24 00:01:27,839 --> 00:01:32,800 This enables retrieval-augmented generation, RAG, with company documents, personal files, 25 00:01:32,800 --> 00:01:35,239 or specialized databases. 26 00:01:35,239 --> 00:01:40,400 Tools also support the analysis of images, audio, and other non-text inputs, enabling 27 00:01:40,400 --> 00:01:44,639 vision capabilities, voice understanding, and multimodal reasoning. 28 00:01:44,639 --> 00:01:49,800 Additionally, they extend beyond built-in constraints by maintaining conversation memory 29 00:01:49,800 --> 00:01:54,760 across sessions and processing information that exceeds the context window size. 30 00:01:54,760 --> 00:02:00,400 Finally, tools interact with APIs, software, and digital services, allowing LLMs to take 31 00:02:00,400 --> 00:02:05,519 actions that a traditional computer program would perform in response to user requests. 32 00:02:05,519 --> 00:02:09,639 Without tools, LLMs rely purely on patterns in their training data. 33 00:02:09,639 --> 00:02:14,559 This often leads to hallucinations where the model confidently makes things up. 34 00:02:14,559 --> 00:02:18,360 This problem is especially noticeable in tasks like math or logic. 35 00:02:18,360 --> 00:02:20,679 Here's an example. 36 00:02:20,679 --> 00:02:24,839 Ask an LLM which 371 multiplied by 492. 37 00:02:24,839 --> 00:02:29,399 It might respond with 158,213. 38 00:02:29,399 --> 00:02:30,399 The answer's wrong. 39 00:02:30,399 --> 00:02:33,320 It's just guessing from patterns in its training data. 40 00:02:33,320 --> 00:02:36,839 So, how do you move from guessing to precision? 41 00:02:36,839 --> 00:02:39,119 By giving LLMs tools. 42 00:02:39,119 --> 00:02:43,880 These tools allow models to take actions beyond text generation, enabling them to interact 43 00:02:43,880 --> 00:02:47,559 with real-world data and perform complex tasks. 44 00:02:47,559 --> 00:02:52,240 Think of them like plugins or external apps that extend the AI's capabilities. 45 00:02:52,240 --> 00:02:56,360 For example, with a calculator tool, the LLM no longer guesses. 46 00:02:56,360 --> 00:03:05,679 It uses the calculator to compute 371 multiplied by 492 and gives the correct answer, 182,532. 47 00:03:05,679 --> 00:03:08,979 This simple addition dramatically improves accuracy. 48 00:03:08,979 --> 00:03:10,399 Tools go far beyond math. 49 00:03:10,399 --> 00:03:15,639 They unlock entirely new capabilities like accessing the web, interacting with databases, 50 00:03:15,639 --> 00:03:19,119 building apps, and even generating visualizations. 51 00:03:19,119 --> 00:03:23,720 For example, a web tool can fetch real-time weather, a code tool can write and execute 52 00:03:23,720 --> 00:03:28,919 Python code, a search tool can look up recent news, and a SQL tool can query data from a 53 00:03:28,919 --> 00:03:30,639 business database. 54 00:03:30,639 --> 00:03:34,479 With these tools, your AI assistant isn't just guessing anymore. 55 00:03:34,479 --> 00:03:37,000 It's becoming an intelligent agent. 56 00:03:37,000 --> 00:03:43,000 With tools, LLMs evolve into agentic systems that observe, think, and take actions based 57 00:03:43,000 --> 00:03:45,399 on what they learn from their environment. 58 00:03:45,399 --> 00:03:48,119 This agentic process follows a clear path. 59 00:03:48,119 --> 00:03:53,360 The user asks a question, the LLM selects the right tool, the tool performs the action, 60 00:03:53,360 --> 00:03:55,720 and the LLM provides the response. 61 00:03:55,720 --> 00:04:01,240 It's how LLMs evolve from processing text to taking meaningful, purposeful actions. 62 00:04:01,240 --> 00:04:05,940 So the next time an LLM gives you a strange answer, remember it's not magic. 63 00:04:05,940 --> 00:04:07,860 It's just missing the right tool. 64 00:04:07,860 --> 00:04:12,479 In this video, you learned to understand the limitations of LLMs without tools and the 65 00:04:12,479 --> 00:04:15,919 impact of missing tools on accuracy. 66 00:04:15,919 --> 00:04:20,640 Identify the powerful capabilities that tools provide to LLMs, including accessing private 67 00:04:20,640 --> 00:04:26,799 data, processing multiple modalities, overcoming LLM limitations, and controlling external 68 00:04:26,799 --> 00:04:28,519 systems. 69 00:04:28,519 --> 00:04:33,239 Recognize how tools transform LLMs from guessers into intelligent agents capable of interacting 70 00:04:33,239 --> 00:04:35,359 with real-world data. 71 00:04:35,359 --> 00:04:40,519 Explore how different tools like calculators, web access, and databases enable LLMs to perform 72 00:04:40,519 --> 00:04:42,880 tasks beyond text generation. 73 00:04:42,880 --> 00:04:48,440 Learn how the agentic process works, LLMs selecting the right tool, taking action, and 74 00:04:48,440 --> 00:04:51,359 providing meaningful responses. 75 00:04:51,359 --> 00:04:55,420 Understand how tools are essential for improving precision and moving from text generation 76 00:04:55,420 --> 00:04:56,440 to problem solving.