Will be able to solve problems effectively
Posted: Sun Feb 16, 2025 8:18 am
Much of our work involves document analysis to find drugs by active ingredient, competitor research to identify dependencies, and creating unique products that meet your needs, and so on.
document analysis
We can then string these prompts together like threads, but china telegram data that requires engineering skills. For example, if you have a prompt for a briefing with your client that prompts them to write a technical brief to your marketing or strategic planning department. These in turn create prompts for developing hypotheses for market research. This can include up to 15 different prompts that can be combined into one functionality and create artificial intelligence that automates a significant portion of the tasks.
With the help of such agents, you can effectively solve tasks, even while on vacation at sea. This agent can perform various functions, and the key task is to create prompts in such a way as to narrow the range of actions and the context in which they are performed as much as possible.
creating prompts
And how it works, in short: it considers what needs to be done, performs the action, evaluates its results, and decides whether they meet its expectations. If so, it goes back to the beginning of the cycle.
You can think of it as a diagram where it automatically breaks down tasks into separate stages (tasks), executes them, stores the results, and returns the finished product to you.
diagram - working with neural networks
To get more specific, let's look at how this works in practice.
For example, we created an agent that conducts marketing research.
We simply write a request to it: “Analyze five shoe brands .” It automatically breaks this task into subtasks, performs them, and we check the results. We can evaluate what we like and what we don’t, add additional tasks, and monitor the execution in real time. Each such task usually has dozens or hundreds of prompts built into it. To create such an agent, you need to interact with all stakeholders, break the task into components, and integrate them into the system. However, now this does not require large expenditures on new research or hiring new employees.
process optimization
Another example is cold mailing optimization.
We enter a task for the agent: to create a personalized cold email for customers in a specific market. The agent independently goes to LinkedIn, analyzes profiles and generates a personalized email based on this information. We observe the process in real time, evaluate the speed of task processing. Let's consider the results obtained.
The agent independently determines the necessary information from profiles to create an effective personalized letter for the target client. He demonstrates the ability to quickly adapt to tasks and generate content aimed at achieving sp
document analysis
We can then string these prompts together like threads, but china telegram data that requires engineering skills. For example, if you have a prompt for a briefing with your client that prompts them to write a technical brief to your marketing or strategic planning department. These in turn create prompts for developing hypotheses for market research. This can include up to 15 different prompts that can be combined into one functionality and create artificial intelligence that automates a significant portion of the tasks.
With the help of such agents, you can effectively solve tasks, even while on vacation at sea. This agent can perform various functions, and the key task is to create prompts in such a way as to narrow the range of actions and the context in which they are performed as much as possible.
creating prompts
And how it works, in short: it considers what needs to be done, performs the action, evaluates its results, and decides whether they meet its expectations. If so, it goes back to the beginning of the cycle.
You can think of it as a diagram where it automatically breaks down tasks into separate stages (tasks), executes them, stores the results, and returns the finished product to you.
diagram - working with neural networks
To get more specific, let's look at how this works in practice.
For example, we created an agent that conducts marketing research.
We simply write a request to it: “Analyze five shoe brands .” It automatically breaks this task into subtasks, performs them, and we check the results. We can evaluate what we like and what we don’t, add additional tasks, and monitor the execution in real time. Each such task usually has dozens or hundreds of prompts built into it. To create such an agent, you need to interact with all stakeholders, break the task into components, and integrate them into the system. However, now this does not require large expenditures on new research or hiring new employees.
process optimization
Another example is cold mailing optimization.
We enter a task for the agent: to create a personalized cold email for customers in a specific market. The agent independently goes to LinkedIn, analyzes profiles and generates a personalized email based on this information. We observe the process in real time, evaluate the speed of task processing. Let's consider the results obtained.
The agent independently determines the necessary information from profiles to create an effective personalized letter for the target client. He demonstrates the ability to quickly adapt to tasks and generate content aimed at achieving sp