What is RAG? Describe the sequential stages of the process.

RAG (Retrieval-Augmented Generation) is an approach in natural language processing that combines generative models with a retrieval mechanism to improve the quality of responses.

Process stages:

1. User query — the user asks a question or enters a request.
2. Retrieval of relevant information — the system searches in knowledge bases, documents, or external sources for information related to the query.
3. Aggregation of found data — selected documents or text fragments are collected for input into the generative model.
4. Response generation — a generative model (e.g., transformer) uses the obtained information to create a detailed and accurate answer.
5. Result output — the generated answer is returned to the user.

This approach allows:

• To use current and specific information.
• To improve the accuracy and relevance of responses.
• To reduce the likelihood of generating fabricated facts.

In Python, libraries for search (e.g., FAISS) and generation models (e.g., Hugging Face Transformers) are often used to implement RAG.