Fine-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.
In Simple Terms
Think of it as a musician who already knows music learning your band's setlist and style.
Detailed Explanation
Fine-tuning takes a base model (e.g. GPT, Llama) and continues training on a curated dataset. That adapts the model to your domain, tone, or task without building from scratch. When to use it: when prompt engineering or RAG is not enough and you need consistent style, terminology, or behavior. Common mistakes: fine-tuning on tiny or noisy data, or expecting it to fix fundamental capability gaps.
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 moreRetrieval-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.
Read moreWant to Implement AI in Your Business?
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