Introduction to the Customization of LLMs

Article 5: LLM Series articles, previous article Understanding (LLMs): How Do They Collect Data? | LinkedIn

In modern large language models (LLMs) like ChatGPT, customization plays a key role. While the master LLM—the main model—comes pre-trained with a broad set of knowledge, users often need the model to focus on their specific needs, whether it's handling their own data, providing specialized information, or answering domain-specific questions. However, when users request these custom capabilities, they do so without changing the master LLM itself. Instead, the master LLM provides technologies that allow for these custom needs to be met while keeping the core model intact.

When customers or organizations need the LLM to focus on their unique data or knowledge, they can rely on the master LLM for support. The master LLM provides three main customization methods: RAG, fine-tuning, and custom GPT. These methods help users build what we call custom sub LLMs (or child LLMs), tailored to handle their specific requirements.

Think of the master LLM as a main brain with a wealth of solid, pre-trained knowledge that doesn’t change “unless done by the main owner of the master LLM”. It is designed using mathematical structures, algorithms, and static training data. But it’s also flexible enough to assist users in creating custom LLMs for their own needs, based on external data or specific requests.

It’s important to note that when the master LLM helps build custom LLMs, it does so without altering its own core knowledge base or thinking. Instead, it supports these child LLMs by using its existing capabilities to interpret new data, answer specific queries, or integrate user-provided information. It’s as if the master brain is lending its thought process to assist in handling specialized tasks, but it doesn’t change its own thinking—it's more like an assistant that helps others with custom tasks while keeping its own knowledge intact.

This approach allows users to build small, specialized LLMs for their own purposes, such as answering questions or managing specific types of data. These child LLMs work together with the master LLM to offer precise, personalized responses based on the user’s needs.

Master LLM and Custom Sub LLMs: How They Work Together

The Master LLM (represented as a large central circle) is the core model, like ChatGPT’s master LLM, which contains general-purpose knowledge trained across a wide variety of topics and domains. This master LLM remains unchanged and serves as the base for all interactions, ensuring that the model can respond across many different areas without being specifically fine-tuned for one.

Around the master LLM are customer-specific sub LLMs (represented by smaller connected circles). Each customer-specific LLM is tailored for a particular user, business, or organization. These custom sub LLMs do not modify the master LLM, but rather, they are created to address specific needs through three key methods RAG (Retrieval-Augmented Generation),Fine-Tuning and Custom GPT.

Each customer-specific sub LLM acts as a child LLM, integrating seamlessly with the master LLM while maintaining its own specialization. This flexible architecture allows customers to create powerful, tailored solutions for their own unique needs, without impacting the broader model’s general knowledge and capabilities.

1. Retrieval-Augmented Generation (RAG):

• What it is: RAG retrieves real-time data from external sources, like databases or documents, to enhance the LLM’s responses. However, it does not modify the master LLM itself. Instead, it creates a custom-specific sub LLM or child LLM that pulls external data in real-time to provide accurate, up-to-date answers.
• How it works: The master LLM processes the initial query, and then the RAG component retrieves relevant data from external sources. This data is combined with the pre-existing knowledge of the master LLM to generate a response. The real-time data retrieval allows for dynamic responses without permanently altering the core LLM.
• In simple terms: RAG is like adding a "live search engine" to the LLM that helps it access and use real-time information for specific queries, while creating a custom sub LLM to handle the specific needs of that task.
• Key point: RAG does not update the master LLM, but instead creates a child LLM that integrates real-time data retrieval.
• Example-Healthcare Application: A custom sub LLM for a medical institution retrieves real-time patient records and the latest medical research papers to generate responses. The custom sub LLM created with RAG doesn’t change the master LLM but accesses relevant external data for more accurate answers

2. Fine-Tuning:

• What it is: Fine-tuning involves training a model on specific datasets to make it more specialized for certain tasks. However, it does not modify the master LLM. Instead, it creates a custom-specific sub LLM (or child LLM) that is specialized in the area it was fine-tuned on.
• How it works: Developers provide specialized data to fine-tune a model on a specific domain or topic. The resulting custom sub LLM can now handle queries within that specialized area but doesn’t alter the core model.
• In simple terms: Fine-tuning is like giving the LLM a focused training course that creates a new, specialized child LLM for specific tasks, but without changing the master LLM.
• Key point: Fine-tuning creates a custom sub LLM for specific tasks and does not modify the master LLM.
• Example-Legal Application: A law firm fine-tunes a model on a dataset of past legal cases to create a custom sub LLM that specializes in legal queries. This fine-tuned model does not update the master LLM, but it becomes an expert in handling specific legal queries.

3. Custom GPT:

• What it is: Custom GPT allows users to define specific behaviors, rules, and responses for the LLM without changing its underlying architecture or training. This creates a custom-specific sub LLM that adheres to those user-defined rules and documents.
• How it works: Users upload custom documents or define rules that guide the model’s responses, creating a custom GPT model that operates based on the user’s needs, but again, it doesn’t modify the master LLM.
• In simple terms: Custom GPT is like setting up a custom rulebook that the LLM follows for specific queries, but it doesn’t alter the core model or its original knowledge.
• Key point: Custom GPT creates a custom sub LLM with user-defined rules, separate from the master LLM.
• Example-Retail Company: A retail company uses custom GPT to create a custom sub LLM that answers customer queries based on company-specific return policies and product information. This model uses static documents but doesn’t change the master LLM. ?