What are LLM Applications?

Saad Afzal

Saad Afzal

· 3 min read
Artificial intelligence

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.


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.

Saad Afzal

About Saad Afzal

With a Master's degree in Structural Engineering, I began my journey in engineering consultancy, where I discovered my passion for automation and software development. As I delved deeper, I integrated Python scripts into my workflows, revolutionising structural design and analysis.

Driven by a desire to embrace the scalability of web applications, I transitioned into full-stack development and cloud engineering. Through relentless self-study, I honed my skills and collaborated with esteemed organizations to develop cutting-edge solutions. Today, I specialize in architecting robust systems and leveraging cloud technologies to create scalable, secure, and user-centric applications.

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