You can use prompt engineering, RAG, and fine-tuning to gain better response from Large Language Models (LLMs). The LLMs can be used even more effectively when they are paired with agents, functions, and tools that enhance their capabilities.
Agents
See the video to learn about agents and how they can optimize LLMs.
Agents are software programs that act like intermediaries between humans and LLMs. They can take user input, translate it into prompts for the LLM, and interpret the LLM's output back into natural language. Agents can also help manage the interaction between the user and the LLM, ensuring that the conversation is productive and efficient. There are many different types of agents; each with its own strengths and weaknesses. Some agents are designed to be general-purpose, while others are specialized for specific tasks. The best agent for a particular use case depends on the specific needs of the user.
Functions
Functions are small units of code that perform specific tasks. LLMs can be augmented with functions that provide them with additional capabilities, such as access to external data sources or the ability to perform complex calculations. Functions can be written in various programming languages, and they can be easily integrated into LLM applications. Using functions can significantly improve the performance of LLMs on specific use cases. For example, an LLM that is used for generating code could be augmented with a function that can connect to a code repository and retrieve relevant code snippets. This allows the LLM to generate more accurate and complete code.
Tools
Tools are software programs that can be used to interact with LLMs. These tools can help users to create prompts, manage LLM interactions, and interpret LLM output. There are various tools available for LLMs, ranging from simple text editors to complex programming frameworks. The tools used depend on the specific needs of the user. For example, a developer who uses an LLM to generate code might use a text editor to create prompts and a debugging tool to inspect LLM output.
Scenario Using the Agent, Functions, and Tool Interaction
Consider a scenario where a user wants to generate a marketing report. They can use an agent to create a prompt for the LLM that specifies the desired content and format for the report. The agent can then use a function to access a company's data warehouse and retrieve relevant data for the report. Finally, the LLM can generate the report based on the data and the user's prompt.
In this example, the agent acts as a bridge between the user and the LLM, ensuring that the user's request is understood and the LLM's output is relevant and actionable. The function provides the LLM with access to the necessary data, while the LLM does the heavy lifting of processing the data and generating the report. By effectively using agents, functions, and tools, users can streamline their LLM workflows, improve the quality of their results, and gain a deeper understanding of the capabilities of these powerful tools.