Welcome to this video on when to call tools manually.
In this video, you will explore how large language models or LLMs can suggest actions
using tools, understand the critical role of agents in automating these actions, and
learn why manual invocation can provide greater control over AI systems.
You will also examine the benefit of manual invocation, including improved safety, cost control, and accuracy.
Imagine you're an AI developer building intelligence systems for a cutting-edge financial technology company.
One day, your large language model or LLM suggests automatically updating sensitive
financial databases based on its predictions.
Sounds convenient, right?
But it also comes with significant risks.
A single mistake could lead to inaccurate reporting, financial losses, or even regulatory issues.
LLMs can suggest actions using tools.
But should you let them execute these actions automatically?
In this video, you'll explore why manual tool invocation can give you more control,
ensuring accuracy, safety, and smarter decision-making.
LLMs can be equipped with knowledge of various tools which are specific actions or functions
that they can use to perform tasks.
When prompted, they can suggest which tools to use and what specific details or parameters are needed for the task.
For example, if the tool is a weather API, the parameters could be the location and date.
This capability makes LLMs powerful problem solvers, capable of analyzing inputs and recommending
the most effective course of action.
However, simply suggesting a tool isn't enough.
Understanding why a particular tool is recommended and how the parameters it needs affect the
outcome is critical for building reliable and efficient AI agents.
This ensures that the system functions as intended, producing accurate and meaningful results.
Agents are designed to automatically execute the tools suggested by LLMs.
The process begins with the user prompt, which triggers the LLM to suggest the appropriate tool.
Once the LLM identifies the tool and its parameters, the agent takes over and executes the tool
without any human intervention.
Finally, the result is returned to the user.
While this process is highly efficient, it's important to ensure that the agent's actions
align with the intended goals, as no manual checks are involved in execution.
While agents can automate things, there are a few important reasons why you might want to take control yourself.
First, safety.
When you manually invoke a tool, you can prevent any unintended actions that could cause problems.
Then cost control.
By doing it yourself, you avoid unnecessary API calls that could end up costing you more than expected.
And finally, accuracy.
When you're in charge, you ensure that the tool is being used correctly with the right
parameters so you get the most accurate results.
So, even with automation, manual invocation gives you that extra level of control and peace of mind.
By manually invoking tools, you maintain oversight of the actions being taken, ensuring that
everything aligns with your intentions.
You can validate inputs and outputs, making sure the tool is being used correctly and the results are accurate.
You ensure that only safe and necessary operations are performed, reducing the risk of unintended consequences.
This level of control allows you to stay in the driver's seat and make adjustments as
needed, offering more precision and reliability than automation alone.
While automation has its advantages, manual tool invocation empowers you with greater
control, safety, and precision.
Choose the approach that best fits your needs.
In this video, you learned to recognize the risks of automatic tool execution and the
importance of manual control for safety and precision.
Understand how LLMs suggest tools and parameters and why it's crucial to evaluate these suggestions for reliable outcomes.
Explore the role of agents in executing LLM tools and the benefits and limitations of automation.
Evaluate the benefits of manual invocation, including enhanced safety, cost control, and accuracy for smarter decision making.