1 00:00:03,000 --> 00:00:07,079 Welcome to this video on when to call tools manually. 2 00:00:07,079 --> 00:00:11,920 In this video, you will explore how large language models or LLMs can suggest actions 3 00:00:11,920 --> 00:00:16,600 using tools, understand the critical role of agents in automating these actions, and 4 00:00:16,600 --> 00:00:21,280 learn why manual invocation can provide greater control over AI systems. 5 00:00:21,280 --> 00:00:28,719 You will also examine the benefit of manual invocation, including improved safety, cost control, and accuracy. 6 00:00:28,719 --> 00:00:34,240 Imagine you're an AI developer building intelligence systems for a cutting-edge financial technology company. 7 00:00:34,240 --> 00:00:38,799 One day, your large language model or LLM suggests automatically updating sensitive 8 00:00:38,799 --> 00:00:41,599 financial databases based on its predictions. 9 00:00:41,599 --> 00:00:43,599 Sounds convenient, right? 10 00:00:43,599 --> 00:00:45,880 But it also comes with significant risks. 11 00:00:45,880 --> 00:00:51,799 A single mistake could lead to inaccurate reporting, financial losses, or even regulatory issues. 12 00:00:51,799 --> 00:00:54,959 LLMs can suggest actions using tools. 13 00:00:54,959 --> 00:00:57,880 But should you let them execute these actions automatically? 14 00:00:57,880 --> 00:01:02,240 In this video, you'll explore why manual tool invocation can give you more control, 15 00:01:02,240 --> 00:01:05,559 ensuring accuracy, safety, and smarter decision-making. 16 00:01:05,559 --> 00:01:11,139 LLMs can be equipped with knowledge of various tools which are specific actions or functions 17 00:01:11,139 --> 00:01:13,639 that they can use to perform tasks. 18 00:01:13,639 --> 00:01:19,839 When prompted, they can suggest which tools to use and what specific details or parameters are needed for the task. 19 00:01:19,839 --> 00:01:25,819 For example, if the tool is a weather API, the parameters could be the location and date. 20 00:01:25,819 --> 00:01:31,120 This capability makes LLMs powerful problem solvers, capable of analyzing inputs and recommending 21 00:01:31,120 --> 00:01:33,300 the most effective course of action. 22 00:01:33,300 --> 00:01:36,980 However, simply suggesting a tool isn't enough. 23 00:01:36,980 --> 00:01:40,580 Understanding why a particular tool is recommended and how the parameters it needs affect the 24 00:01:40,580 --> 00:01:44,639 outcome is critical for building reliable and efficient AI agents. 25 00:01:44,639 --> 00:01:50,580 This ensures that the system functions as intended, producing accurate and meaningful results. 26 00:01:50,580 --> 00:01:54,639 Agents are designed to automatically execute the tools suggested by LLMs. 27 00:01:54,639 --> 00:02:00,199 The process begins with the user prompt, which triggers the LLM to suggest the appropriate tool. 28 00:02:00,199 --> 00:02:04,959 Once the LLM identifies the tool and its parameters, the agent takes over and executes the tool 29 00:02:04,959 --> 00:02:06,879 without any human intervention. 30 00:02:06,879 --> 00:02:10,279 Finally, the result is returned to the user. 31 00:02:10,279 --> 00:02:14,639 While this process is highly efficient, it's important to ensure that the agent's actions 32 00:02:14,639 --> 00:02:20,520 align with the intended goals, as no manual checks are involved in execution. 33 00:02:20,520 --> 00:02:25,800 While agents can automate things, there are a few important reasons why you might want to take control yourself. 34 00:02:25,800 --> 00:02:27,600 First, safety. 35 00:02:27,600 --> 00:02:33,440 When you manually invoke a tool, you can prevent any unintended actions that could cause problems. 36 00:02:33,440 --> 00:02:34,660 Then cost control. 37 00:02:34,660 --> 00:02:40,679 By doing it yourself, you avoid unnecessary API calls that could end up costing you more than expected. 38 00:02:40,679 --> 00:02:42,720 And finally, accuracy. 39 00:02:42,720 --> 00:02:46,440 When you're in charge, you ensure that the tool is being used correctly with the right 40 00:02:46,440 --> 00:02:49,639 parameters so you get the most accurate results. 41 00:02:49,639 --> 00:02:55,559 So, even with automation, manual invocation gives you that extra level of control and peace of mind. 42 00:02:55,559 --> 00:03:00,440 By manually invoking tools, you maintain oversight of the actions being taken, ensuring that 43 00:03:00,440 --> 00:03:02,960 everything aligns with your intentions. 44 00:03:02,960 --> 00:03:08,440 You can validate inputs and outputs, making sure the tool is being used correctly and the results are accurate. 45 00:03:08,440 --> 00:03:14,860 You ensure that only safe and necessary operations are performed, reducing the risk of unintended consequences. 46 00:03:14,860 --> 00:03:18,279 This level of control allows you to stay in the driver's seat and make adjustments as 47 00:03:18,279 --> 00:03:22,960 needed, offering more precision and reliability than automation alone. 48 00:03:22,960 --> 00:03:27,440 While automation has its advantages, manual tool invocation empowers you with greater 49 00:03:27,440 --> 00:03:30,000 control, safety, and precision. 50 00:03:30,000 --> 00:03:33,039 Choose the approach that best fits your needs. 51 00:03:33,039 --> 00:03:37,899 In this video, you learned to recognize the risks of automatic tool execution and the 52 00:03:37,899 --> 00:03:41,800 importance of manual control for safety and precision. 53 00:03:41,800 --> 00:03:48,440 Understand how LLMs suggest tools and parameters and why it's crucial to evaluate these suggestions for reliable outcomes. 54 00:03:48,440 --> 00:03:54,220 Explore the role of agents in executing LLM tools and the benefits and limitations of automation. 55 00:03:54,220 --> 00:04:00,520 Evaluate the benefits of manual invocation, including enhanced safety, cost control, and accuracy for smarter decision making.