Fine-Tuning
Fine-tuning is training a base model further on your examples so it better matches a style or task. It does not automatically know your latest documents.
In depth
Unlike RAG, fine-tuning changes weights. It is expensive to run well and easy to do too early. Default: hosted API + prompt + RAG if you have files. Fine-tune after a measured eval failure.
See RAG vs fine-tuning.
Real example
A support bot that must always use a brand voice after RAG still sounds generic — then a small fine-tune can be an experiment.
Tools & calculators
Related terms
RAG (Retrieval-Augmented Generation)
Retrieval-Augmented Generation (RAG) is a pattern where the system searches a document store, then asks an LLM to answer using the retrieved chunks — ideally with citations.
Embeddings
Embeddings are numeric vectors that represent text (or images) so similar meanings sit close together. RAG uses them to find relevant chunks.
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