The term Generative AI is everywhere in the business world these days, accompanied by a plethora of technical terms that can be overwhelming. Concepts like foundational large language models (LLM), Retrieval-Augmented Generation (RAG), model fine-tuning, and building a domain specific custom language models might seem like a foreign language, even to tech-savvy individuals.
In this blog post, we’ll break down these complex concepts using relatable culinary metaphors and discuss which technique is best suited for each situation. By imagining AI as a master chef with a range of tools and recipes, we’ll explore the world of Generative AI in a way that’s easy to digest.
The Foundational Large Language Models: The Master Chefs
Think of the base foundational large language model as the master chef. This chef has years of experience, a vast range of recipes, and an exceptional understanding of different cuisines. Just like a master chef, a foundational language model has been trained on a massive dataset. This extensive training allows the model to understand and generate human-like content efficiently.
When you interact with applications like ChatGPT or Gemini and ask a question, you are engaging with the foundational models behind them, which are responsible for answering your query. Examples of foundational models include OpenAI’s GPT-4, GPT-3.5, as well as Meta’s LLaMA 3.1 etc.
The foundational model has a knowledge cutoff, meaning it can only provide answers based on information available up to a certain date, which may result in outdated responses for events or developments that occurred afterward.
When you need a general-purpose AI capable of handling a wide range of tasks — such as generating articles, creating chatbots, or even composing music — the foundational model is your go-to chef.
Retrieval-Augmented Generation (RAG): The Chef with a Secret Recipe Book
Now, imagine our master chef has access to a secret recipe book that contains specialized knowledge or modern cooking techniques that the chef was not initially trained on. Upon request from the diner, the chef learns these new techniques and creates a new dish, combining existing knowledge with updated insights from the book. This is analogous to Retrieval-Augmented Generation (RAG). RAG merges the knowledge of the base model with a retrieval mechanism to access specific, up-to-date information from external data sources, enhancing the accuracy and relevance of the generated content.
RAG addresses the base model’s knowledge cutoff by providing access to up-to-date information without modifying the base model. The effectiveness of RAG depends on the quality and relevance of the information in the dataset; poorly maintained or biased data can compromise the output.
RAG is ideal for scenarios where AI needs to generate content with specific, recent information, such as a chatbot providing details on the latest products or responding to company internal policies that are not publicly available and beyond the foundational model’s knowledge.
Fine-Tuning a Model: The Chef Learning a New Cuisine
Picture our master chef deciding to specialize in a new cuisine, such as Italian and Asian fusion. To do this, the chef will learn specific techniques, ingredients, and recipes to create fusion dishes that will have unique tastes, colors, fragrances, and more. Similarly, fine-tuning a model involves further training a foundational model on a specific dataset to specialize in a particular domain, enhancing its capabilities within that scope.
Fine-tuning adapts the model to a specific dataset, enabling it to better capture the nuances of a domain or style. However, fine-tuning can lead to overfitting, where the model excels in one domain but struggles in more general areas. Additionally, fine-tuning requires significant technical expertise, as well as data and computational resources.
Fine-tuning is ideal for tasks that demand domain-specific expertise, especially when you want the model to adapt to a particular style or vocabulary, and when you have a limited dataset to train the foundational model. For example, a healthcare chatbot may need to understand medical terminology and answer patients’ specific queries, while a finance chatbot specializes in solving financial problems. For example — Med-PaLM, tuned on the PaLM base model, is a fine-tuned model designed to provide answers to medical questions, while FinGPT uses LLaMA-2 as a base model and is fine-tuned for finance.
Domain-Specific Custom Model: The Super-Focused Chef
Imagine our chef decides to create an entirely new cuisine by blending unique ingredients and techniques. This is like building a custom model from scratch. You gather data, design your architecture, and train your model to create something entirely new and tailored to your needs.
Custom models focus tightly on specific tasks and domains, providing precise and relevant outputs. A domain-specific custom model can generate accurate answers relevant to the domain by being purposefully designed for a particular problem.
Building a custom model makes sense when you have a large dataset of high-quality data and want the model to perform a specific task exceptionally well. For example, a model trained on a specific dataset about a disease can effectively detect that disease. In our chef analogy, this chef has learned exclusively how to cook that specific cuisine exceptionally well after undergoing rigorous training and can only prepare dishes within that new cuisine.
For example — BloombergGPT is a custom-built large language model trained on Bloomberg’s financial data, capable of addressing a diverse set of financial tasks.
Conclusion
In conclusion, Foundational large language models are similar to master chefs with broad expertise, while Retrieval-Augmented Generation (RAG) is like a chef using a secret recipe book to access up-to-date information. Fine-tuning a model enhances its capabilities in specific domains, much like a chef learning a new cuisine. Similarly, building a domain-specific custom model is equivalent to creating a new cuisine, tailored to precise tasks. I hope this article has simplified some of the most commonly used buzzwords in the Generative AI world and will help you determine which technique is right for you. Happy reading!
