— Techniques
Fine-Tuning
Adjusting a pre-trained AI model on your specific data to change its behaviour.
What is Fine-Tuning?
Fine-tuning is the process of further training a pre-trained LLM on your custom dataset (examples of inputs + desired outputs) to teach it your specific style, voice, or task patterns. Use cases: brand-voice locking, specialised classification, domain-specific tone. In 2026, fine-tuning is increasingly replaced by RAG + good prompting for most tasks because it's cheaper and faster to iterate.
— Related
Terms connected to Fine-Tuning
Techniques
RAG (Retrieval-Augmented Generation)
Giving an AI access to your private documents so it can answer questions about them.
Open →Techniques
LoRA (Low-Rank Adaptation)
An efficient way to fine-tune large models by training only a small set of new parameters.
Open →Techniques
Embedding
A numeric representation of text (or image) that captures its meaning.
Open →Techniques
Training Data
The text, images, or examples used to teach an AI model what to do.
Open →— Apply this
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