Executive summary:
There are two competing paths for turning generalist large language models into specialists: fine-tuning and retrieval-augmented generation (RAG).
LLM fine-tuning involves retraining an existing AI model on your domain-specific data. This approach offers deep expertise but requires significant computational resources and ongoing maintenance.
RAG combines a general-purpose AI with a customizable knowledge base. It's more flexible, easier to update, and provides transparent sourcing of information. RAG excels at incorporating the most current data without extensive retraining.
Both methods have their strengths:
For optimal results, you can combine RAG with fine-tuning. A fine-tuned model can serve as the foundation for a RAG system, providing both deep expertise and up-to-date information access. For the ultimate in configurability, flexibility, agility, and explainability, look into a cognitive hive AI (CHAI) architecture. CHAI takes a modular building-block approach where you can fine-tune individual components, give individual components access to specialized resources (RAG), or both.
Talbot West specializes in guiding enterprises through AI tool selection, implementation, and governance. Our expertise helps you leverage the right AI approach (CHAI, fine-tuning a standard LLM, RAG) for your unique business needs.
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Retrieval-augmented generation (RAG) and fine-tuning are two approaches for transforming a general-purpose large language model (LLM) into a domain specialist.
Retrieval-augmented generation combines the power of generative AI with the precision of targeted data retrieval. It enhances AI's ability to give accurate, context-specific responses.
A RAG system has two main components:
Let's take a look at how it works:
RAG is useful for companies that want an AI expert who knows everything about their business—without the HR overhead.
If you’d like help implementing RAG in your organization, let’s talk.
LLM fine-tuning transforms a general-purpose AI model into a specialized expert for specific tasks or domains. It's like taking a highly educated generalist and giving them focused training to become a subject matter expert.
Here's a deeper look at how fine-tuning works:
With fine-tuning, you need to be careful not to overfit the model. Overfitting occurs when you overtrain the model, with the result that it can no longer generalize effectively to information outside its narrow scope.
If you’d like help fine-tuning your LLM, get in touch, and let’s have a free consultation.
LLM fine-tuning and RAG offer distinct approaches to driving specialization in AI. Let's take a look at how they compare:
| LLM fine-tuning | Retrieval-augmented generation | |
|---|---|---|
Specialization | Deep specialization in a specific domain or task | Broader knowledge scope, less domain-specific |
Flexibility | Less flexible; specialization is fixed after training | Highly flexible; can adapt to new topics by updating the knowledge base |
Information updates | Requires retraining to incorporate new information | Can be updated by modifying the knowledge base without retraining |
Resource requirements | Computationally intensive; may require significant GPU resources | No additional compute required |
Maintenance | More challenging; requires periodic retraining | Easier; primarily involves updates to the knowledge base |
Up-to-date information | Limited to information available during training | Accesses the most current data in the knowledge base |
Transparency | Less transparent; knowledge embedded in model parameters | More transparent; can cite sources of retrieved information |
Performance in specialized tasks | Typically outperforms general LLMs in the specialized domain | Performance depends on the quality and relevance of retrieved information |
Breadth of knowledge | Limited to the scope of fine-tuning data | Only limited by the knowledge base content |
Integration of new domains | Requires additional fine-tuning or training new models | Can add new domains by expanding the knowledge base |
Response generation | Generates responses based on internalized knowledge | Generates responses by combining model knowledge with retrieved information |
Customization | Highly customized to the specific fine-tuning data | Customization through careful curation of the knowledge base |
Scaling | Scaling to new domains may require training multiple models | Can scale to new domains by adding to the knowledge base |
RAG and fine-tuning are complementary approaches. When combined they create a powerful, specialized AI system. Here are some potential approaches:
Cognitive hive AI (CHAI) is a modular approach to AI implementation. Rather than a single, monolithic LLM deployed in your organization, you can configure multiple smaller LLMs to work in conjunction. You can even deploy other types of AI or knowledge management as modules: knowledge graphs, specialized neural networks, large quantitative modules, and much more.
Essentially, CHAI is infinitely configurable and agile. Individual modules can be connected to knowledge sources (RAG), or fine-tuned to specific parameters, without needing to take the same approach to the entire system.
Read more about CHAI use cases and implementation feasibility in our article titled “What is cognitive hive AI?”
Talbot West is here to help you assess the best AI solution for your needs. We’ll guide you through tool selection, implementation, governance, and any other AI issue you’re facing.
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Retrieval augmented generation was first introduced by researchers at Facebook AI (now Meta AI). They detailed the concept in a 2020 paper, which outlined how combining retrieval mechanisms with generative models could enhance information accuracy and relevance.
Fine-tuning LLMs can be a resource-intensive process. The costs fluctuate based on factors such as the size of the model, the complexity of the dataset, and the duration of the fine-tuning process. Although it's generally more economical than training a model from scratch, it still requires substantial resources. For organizations working with limited budgets, there are more cost-effective alternatives available, such as parameter-efficient fine-tuning techniques.
ChatGPT does not incorporate RAG in its base incarnation. You can create a mini RAG-like architecture by building a custom GPT and adding your own documentation to it.
The data requirements for LLM fine-tuning are contingent on the complexity of the task and the desired level of performance. As a general guideline, a dataset ranging from several hundred to a few thousand high-quality, diverse samples is often sufficient. While increasing the sample size can lead to improved results, there's usually a point of diminishing returns beyond which additional data provides minimal benefits.
Fine-tuning is a specific application of transfer learning. While transfer learning broadly encompasses the use of knowledge from one domain to improve performance in another, fine-tuning specifically refers to the process of adapting a pre-trained model for particular tasks.
Factors influencing the timeline include the size of the model, the volume of the dataset, and the computational resources at hand. For smaller models, the process might take just a few hours, while larger models could require several days or even weeks.
The future of RAG is promising, with ongoing research focused on enhancing data retrieval efficiency and expanding integration with diverse data sources. As the technology progresses, RAG is expected to significantly improve AI's capability to deliver real-time, contextually accurate information. This advancement will transform how businesses and applications interact with and utilize data.
While both RAG and semantic search aim to improve information retrieval, they differ in their outputs. RAG retrieves relevant data and uses it to generate new, contextually enriched content. Semantic search, however, focuses on understanding the meaning behind queries to provide the most relevant existing documents or results, without creating new content.
Talbot West bridges the gap between AI developers and the average executive who's swamped by the rapidity of change. You don't need to be up to speed with RAG, know how to write an AI corporate governance framework, or be able to explain transformer architecture. That's what Talbot West is for.
