In this article, we will explore how to optimize LLM using RAG, whereas in previous articles “Large Language Models (LLM)“, we first explored Large Language Models (LLMs) and Prompt Engineering techniques, and later delved into Retrieval-Augmented Generation (RAG) in article “RAG: What It Is and How to Optimize LLMs” explaining its functioning and advantages.
RAG and Prompt Engineering
To understand how to optimize LLM with RAG, it’s important to know that Prompt Engineering is the simplest and least technical method to interact with a large language model (LLM). This approach involves formulating a series of instructions that the model must follow to generate an appropriate output in response to a user’s query.
Unlike RAG (Retrieval-Augmented Generation), prompt engineering requires fewer data, as it only uses those employed during the initial training of the model, and is less costly, utilizing only existing tools and models.
However, prompt engineering has some limitations. It cannot generate output based on updated or changing information since the model relies solely on pre-trained data, which can become outdated. Moreover, the quality of the output heavily depends on the prompt formulation itself; a poorly constructed or ambiguous prompt can lead to incoherent or inaccurate responses.
Despite these limitations, prompt engineering is an intuitive and cost-effective solution, ideal for extracting information on general topics that do not require a high level of detail. It’s particularly useful when quick responses are needed without the necessity to integrate updated or specific data. In such cases, prompt engineering offers an effective way to leverage LLM capabilities without the costs and complexity associated with methods like RAG.
Differences between RAG and Fine-Tuning a Model
To understand how to optimize LLM, it is necessary to recognize that fine-tuning and RAG are two different approaches to improving LLM performance. RAG focuses on integrating external data to enrich the LLM‘s content. In practice, RAG does not modify the base model but allows it to access a set of specific, up-to-date data in real time.
This approach uses minimal computational resources compared to fine-tuning, as it only requires a document or collection of relevant documents. RAG is particularly effective at reducing the risk of “hallucinations,” i.e., incorrect but convincing responses, as it allows the model to refer to verifiable sources.
Fine-tuning, on the other hand, involves modifying the pre-trained model through further training with a smaller, more specific dataset. This process enables the model to learn more targeted patterns and knowledge, which do not require frequent updates. However, fine-tuning requires a significant amount of data and computational resources, as well as substantial time and financial investment. It is ideal for highly specialized applications where the data is stable over time and where in-depth analysis is required.
Key Differences
- Resources: RAG requires fewer computational resources and can be quickly implemented with the addition of specific documents, while fine-tuning requires a large dataset and significant processing resources.
- Data Updates: RAG allows continuous information updates, incorporating new and verifiable data in real time. Fine-tuning, on the other hand, does not allow for frequent updates once the training process is complete.
- Reduction of Hallucinations: RAG has been shown to reduce hallucinations better than fine-tuning since it bases responses on verifiable external data, while fine-tuning requires a complex and costly process to achieve the same result.
- Specialized Applications: Fine-tuning is preferable in contexts where there is a large amount of specific data and adequate resources, and when it is not expected that the data will frequently change. RAG is more suitable for dynamic scenarios where access to up-to-date information is crucial.
Often, combining RAG and fine-tuning can offer the best of both worlds. Fine-tuning can be used to create a solid and specialized foundation for the model, while RAG can be implemented to keep information updated and relevant, improving the accuracy of responses over time.
In summary, the choice between RAG and fine-tuning depends on the specific needs of the project, the available resources, and the nature of the data being worked with.
Differences between RAG and Pretraining
Pretraining and RAG (Retrieval-Augmented Generation) represent two distinct approaches in the training and use of large language models (LLMs). Although both aim to improve the model’s capabilities, they differ significantly in method, required resources, and specific applications.
In the process of optimizing LLM, pretraining is the initial phase of training an LLM, during which the model acquires a basic understanding of language. This process involves using a large generic dataset to create an internal neural network, similar to the human brain’s learning process through the formation of neural connections.
Pretraining requires a huge amount of computational resources, often thousands of GPUs, and is a long and costly process. It is suitable when there is a very large dataset that can significantly influence the model, and there is a desire to provide a basic understanding of a wide range of topics and concepts.
RAG, on the other hand, integrates external, specific information into a pretrained LLM. This method allows the model to access updated and relevant data in real-time, improving the accuracy of responses and reducing the risk of “hallucinations” (erroneous but convincing responses). RAG uses fewer resources compared to pretraining and can be quickly implemented by adding specific documents. It is ideal for scenarios where information needs to be frequently updated and where quick access to specific data sources is required.
Key Differences
- Resources and Time: Pretraining requires a huge amount of computational resources and time, while RAG is much cheaper and faster to implement.
- Purpose and Applications: Pretraining provides a general knowledge base, useful for models that need to understand and generate language on a wide range of topics. RAG, instead, is used to integrate specific and updated information, improving the accuracy of responses for specialized applications.
- Information Updates: RAG allows the model to stay updated with new information in real-time, while pretraining does not foresee frequent updates after the initial phase.
In the next article, we will examine how Revelis is implementing the Retrieval-Augmented Generation technique in its solutions, sharing case studies and results achieved.
Author: Francesco Scalzo
