Training, Fine-Tuning, RAG, and Prompt Engineering AI Models

Artificial Intelligence (AI) models, particularly large language models (LLMs), have made significant strides in recent years. Their capabilities, from generating human-quality text to solving complex problems, are continually expanding. To optimise these models for specific tasks, four key methods can be employed: training from scratch, fine-tuning, prompt engineering and Retrieval Augmented Generation (RAG). However, the effectiveness of these techniques varies significantly in terms of cost and complexity, so this article aims to provide a high level overview of each method.

Training from scratch: The Foundation

Training from scratch is the initial phase of an AI model’s development. It involves feeding the model vast amounts of data to learn patterns, relationships, and rules. This data can be text, images, or other forms of information. The model adjusts its internal parameters to minimize errors and improve its ability to generate accurate outputs. Think of training as teaching a child the basics of language and knowledge.

Training large AI models can be extremely expensive due to the computational resources required. This includes powerful hardware like GPUs and TPUs, as well as significant energy consumption. The cost of training can vary widely depending on the model’s size, the quality and quantity of data, and the chosen hardware.

Fine-Tuning: Specialisation

Fine-tuning is a subsequent step where a pre-trained model is adapted to perform specific tasks. This involves training the model on a smaller, more specialized dataset. For instance, a general-purpose LLM can be fine-tuned on medical literature to become a medical chatbot. Fine-tuning leverages the knowledge acquired during training and refines it for a particular domain. It’s akin to specializing in a particular subject after a general education.

Fine-tuning is generally less expensive than training, as it requires a smaller dataset and fewer computational resources. However, the cost can still be significant, especially for large models and complex tasks.

Prompt Engineering: Guiding the Model

Prompt engineering is the process of crafting effective prompts or instructions to guide the model’s output. A well-designed prompt can significantly influence the quality and relevance of the model’s response. For example, a prompt like “Write a poem about a robot who dreams of becoming a gardener” will elicit a different response than “Summarise the plot of the novel ‘1984’.” Prompt engineering is essential for harnessing the full potential of AI models and ensuring they generate the desired outcomes. It’s like providing specific questions or assignments to a student.

Prompt engineering is typically a relatively low-cost process, as it primarily involves human expertise. However, the quality of the prompts can significantly impact the model’s performance, so investing in skilled prompt engineers can be worthwhile.

RAG: Enhancing Model Performance

RAG (Retrieval Augmented Generation) is a technique that can significantly enhance the performance of AI models. It involves combining the strengths of retrieval-based and generative approaches. In RAG, a retrieval system is used to find relevant information from a large corpus of text, and then the retrieved information is used to guide the generative model’s output. This can improve the model’s accuracy, coherence, and factuality.

The cost of implementing RAG can vary depending on the complexity of the retrieval system and the size of the corpus. However, it can be a worthwhile investment, as it can significantly improve the quality of the model’s output.

Key Differences and Relationships

Training and fine-tuning are sequential processes, with fine-tuning building upon the foundation established by training.

• Training: This is the initial phase where the model learns from a massive dataset, acquiring general knowledge and understanding. It’s like teaching a child the basics of language and knowledge.
• Fine-tuning: Once the model has a solid foundation, fine-tuning is used to specialize it for specific tasks. It’s like specializing in a particular subject after a general education. For example, a general-purpose LLM can be fine-tuned on medical literature to become a medical chatbot.

Prompt engineering is a complementary process that interacts with both training and fine-tuning. While training and fine-tuning shape the model’s overall capabilities, prompt engineering guides its output based on specific instructions. It’s like providing specific questions or assignments to a student. A well-designed prompt can significantly influence the quality and relevance of the model’s response.

RAG (Retrieval Augmented Generation) is a technique that can be integrated into any of these processes to enhance the model’s performance. It involves using a retrieval system to find relevant information from a large corpus of text, and then using that information to guide the generative model’s output. This can improve the model’s accuracy, coherence, and factuality.

In summary:

• Training: Builds the foundation of the model. Expensive due to the computational resources required.
• Fine-tuning: Specialises the model for specific tasks. Less expensive than training from scratch, but still requires computational resources.
• Prompt engineering: Guides the model’s output based on specific instructions. Can be relatively inexpensive, but requires careful consideration and experimentation to ensure the chatbot provides accurate and helpful responses.
• RAG: Enhances the model’s performance by retrieving and incorporating relevant information. The cost depends on the size of the knowledge base and the complexity of the generative model.

Which one do I choose???

While there’s no one-size-fits-all answer, the optimal approach depends heavily on the specific context. From a simplified perspective, we can linearly compare these methods based on their complexity, cost, and flexibility.

Although this seems simple at first glance, the context where these methods are implemented (or combined) may not reflect this linear view. For example, RAG can become more complex and expensive in scenarios involving vast, dynamic, and highly specialised knowledge bases, such as legal or medical domains. In these cases, building and maintaining a comprehensive knowledge base, developing sophisticated retrieval models, and integrating them with a powerful language model can significantly increase development and operational costs. Additionally, if the knowledge base requires frequent updates to reflect new information or regulations, RAG’s ongoing maintenance costs can outweigh the initial investment in LLM fine-tuning. I wrote this article containing some important aspects to consider when preparing data for AI consumption.

Conclusion

Training, fine-tuning, RAG and prompt engineering are interconnected processes that contribute to the development and application of AI models. However, the cost of these processes can be significant, especially for large models and complex tasks. By understanding the factors that influence cost and making informed decisions, organisations can effectively leverage AI to achieve their goals.