Fine-Tuning
The process of further training a pre-trained LLM on a smaller, domain-specific dataset to specialize its behavior for a particular industry or task.
A general-purpose LLM knows a little about everything. A fine-tuned model knows everything about your business. BasaltHQ offers enterprise fine-tuning pipelines that take your historical data—past sales emails that closed deals, legal briefs that won cases, support tickets that achieved high CSAT—and use them to specialize the base model. The result is an agent that doesn't just write generic corporate prose; it writes in your company's exact voice, using your terminology, referencing your products correctly, and adhering to your style guide. Fine-tuning is performed within the BasaltHQ privacy perimeter, ensuring your training data never leaves your tenant.
Related Concepts
See also:
Prompt Engineering
The discipline of designing, testing, and optimizing the textual instructions given to an LLM to maximize the quality, accuracy, and consistency of its output.
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Embedding Model
A neural network that converts raw data (text, images, audio) into dense numerical vectors that capture semantic meaning.
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Transfer Learning
A machine learning technique where a model trained on one task is repurposed as the starting point for a model on a different but related task.