Fine-tuning

What is Fine-tuning?

Fine-tuning is a powerful technique used to improve the accuracy of AI models. It involves training a pre-existing model on a smaller, more specific dataset to learn a new task. This allows the model to make more accurate predictions for the specific task at hand.

Please refer to this mindmap to understand how the responses of foundational models are altered based on the chosen customization technique.

Fine-tuning vs In-Context Learning vs Prompt Engineering

Fine-tuning involves adapting a pre-trained language model by training it on a smaller, domain-specific dataset. While this method is effective for domain adaptation and enhancing accuracy, its most distinguishing advantage is providing structure to the model's responses or completions. However, achieving optimal results through fine-tuning requires a substantial amount of data, computing power, and technical expertise, making the process potentially time-consuming. In-Context Learning (for Building AI Co-Pilots) using the Retrieval Augmented Generation (RAG) pattern enhances large language models (LLMs) by integrating them with external information retrieval systems. Rather than relying exclusively on an LLM's pre-trained knowledge, RAG taps into external sources to provide contextually rich responses. This is crucial since the knowledge within LLMs is confined to a specific cutoff date. Building AI Co-Pilots utilizing this approach demands a considerable amount of technical expertise, given the requirements such as creating embeddings, devising chunking strategies, and establishing a vector database.

Prompt engineering involves crafting a prompt or a series of prompts that guide the language model to produce specific outputs. This method can yield high-quality results with minimal data and computing power. Unlike fine-tuning, prompt engineering is more accessible; it doesn't demand extensive technical knowledge and allows users to utilize any model without the need for customization.