Fine-Tuning

Continuing a pretrained model's training on a smaller, task-specific dataset to specialize its behavior for a particular domain or format.

Fine-tuning and retrieval are the two levers for specializing an agent; the agentic web leans on retrieval, since machine-readable content updates a model's knowledge without retraining it.

term
Fine-Tuning
category
knowledge-memory
short_def
Continuing a pretrained model's training on a smaller, task-specific dataset to specialize its behavior for a particular domain or format.
long_def
Fine-tuning adapts an already-trained model by updating its weights on curated examples, so it learns a style, a domain vocabulary or a structured-output format the base model does not reliably produce. It is a form of transfer learning, distinct from retrieval, which adds knowledge at inference time without changing weights.
see_also
rlhf rag embeddings
etymology_origin
— verify-against-primary-at-build ↗ https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning) — established transfer-learning term in deep learning
related_to
rlhf rag embeddings transformer
contrast_with
Unlike retrieval-augmented generation, which supplies knowledge at query time and changes no weights, fine-tuning bakes behavior into the model by training on examples.
example
A team might fine-tune a model on their support transcripts so it adopts their tone and product vocabulary without needing those examples in every prompt.
source
https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning)
status
active
why_it_matters
Fine-tuning and retrieval are the two levers for specializing an agent; the agentic web leans on retrieval, since machine-readable content updates a model's knowledge without retraining it.
sameAs
https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning)
bridge_entity
rag
last_verified
2026-07-06
md_twin
/glossary/fine-tuning.md

last verified · by

← all The Agentic Web Lexicon · .md · JSON