What is the difference between fine-tuning and a large context window (RAG)? What is the point of RAG and fine-tuning if everything can be placed in the context?

Fine-tuning is the process of additional training of a model on specific data to adapt it to a particular task or domain. As a result, the model 'remembers' features and nuances that were not considered in the initial training.

A large contextual window (for example, in RAG — Retrieval-Augmented Generation) is an approach where a model is provided with a large amount of relevant information (such as documents, knowledge base), which it uses to generate responses. The model itself does not change, but it receives additional context for more accurate and up-to-date answers.

Why not just put everything into the context?

• Context size limitations: even the largest models have a limit on the number of tokens that can be fed at once.
• Data relevance: RAG allows dynamically loading fresh information without retraining the model.
• Specialization: fine-tuning enables the model to better understand and generate within a narrow field, which is difficult to achieve with context alone.

Thus, fine-tuning provides deep adaptation of the model, while RAG offers flexibility and relevance through external data. Both approaches complement each other and solve different tasks.