Retrieval-Augmented Generation (RAG)
RAG is a pattern where an AI model gets up-to-date or private information from a search step before answering, so answers are grounded in your data.
In Simple Terms
Think of it as giving the AI a stack of reference papers before it writes the report.
Detailed Explanation
RAG combines retrieval (e.g. vector or keyword search over documents) with generation: the model sees retrieved chunks and then produces an answer. That reduces hallucination and keeps answers current. When to use RAG: when the model does not know your data or when facts change often. Common mistakes: retrieving too many or too few chunks, or not telling the model to stick to the retrieved context.
Related Terms
Transformer
A transformer is a neural network architecture that uses attention to process sequences (e.g., text or tokens) in parallel rather than step-by-step. It underlies most large language models and many vision and multimodal systems.
Read moreAttention Mechanism
The attention mechanism lets a model focus on different parts of its input when producing each part of the output. It is the core of transformer architectures and enables handling long sequences and rich context.
Read moreFine-Tuning
Fine-tuning is training a pre-trained model on your own data so it gets better at specific tasks or styles while keeping its general abilities.
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