RAG gives a language model relevant documents at the moment it answers, while fine-tuning retrains the model's weights on custom examples. RAG suits changing facts that need citing; fine-tuning suits fixed styles, formats or specialised skills.
Prompt engineering is the practice of designing and testing the instructions, context and examples given to a language model. The aim is reliable, useful output.
Retrieval-augmented generation (RAG) is a pattern that hands a language model relevant passages from your own documents at question time. Its answers then rest on that material, not only on what it learnt in training.
A vector database stores embeddings and quickly finds the ones most similar to a query vector, usually alongside metadata filters. It is the common retrieval layer behind semantic search, recommendations and RAG.
Fine-tuning is further training of an existing model on a smaller, task-specific dataset so it adopts a particular style, format or skill. It changes how a model behaves without the cost of training a new one from scratch.
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