What are LLM Applications?

Large Language Models (LLMs) have revolutionised the way we interact with technology, offering a wide range of applications from simple question-answering to complex code generation. In this blog post, we’ll break down the essential components of LLM applications, explain the concept of “text-in-text-out,” and explore how these models leverage internal and external knowledge to perform various tasks.

The Foundation: Text-In-Text-Out

At the core of LLM applications is the “text-in-text-out” paradigm. The model takes an input text, known as a “prompt,” and generates an output text in response. The prompt can be divided into two main parts:

1. System Prompt: This includes elements such as:
• <task_desc>: Description of the task to be performed.
• : Tools or functions available for use.
• : Sample inputs and outputs to guide the model.
• <chat_history>: Previous interactions in a conversation.
• : Additional information relevant to the task.

2. User Part: This is typically the user’s query or request, denoted as <user_query>.

Leveraging the Model’s Internal Knowledge

When you input a simple question, the LLM uses its internal knowledge to provide an answer. This is straightforward question-answering (QA). However, to extract more precise or context-specific information, you can enhance the prompt:

• Zero-Shot In-Context Learning (ICL): Add a task description (<task_desc>) to guide the model on the nature of the task.
• Few-Shot to Many-Shot ICL: Include examples () to show the model how similar queries have been handled.

Interaction with External Knowledge

Beyond internal knowledge, LLMs interact with the world in three significant ways:

1. External Context and Memory:
• : Information retrieved from external sources, such as databases or search engines. This approach is known as Retrieval-Augmented Generation (RAG).
• <chat_history>: Maintains the flow of conversation, enabling the model to remember previous interactions. This is utilized in memory-augmented models like MemGPT.
2. Predefined Tools/Function Calls:
• LLMs can use predefined tools or call specific functions to perform tasks beyond text generation, such as making calculations or fetching real-time data.
3. Code Generation:
• LLMs can generate code based on the prompt and execute it to provide outputs, making them powerful tools for programming and automation tasks.

Conclusion

LLM applications are built on the “text-in-text-out” foundation, with prompts guiding the model’s behavior. By leveraging internal knowledge and interacting with external data and tools, LLMs perform a wide array of tasks, from answering questions to generating code. Understanding these components helps us better utilize LLMs in various real-world applications.