DefinitionCustomizationTraining

What is fine-tuning?

Fine-tuning is the process of further training a pre-trained AI model on your organization's specific data to customize its behavior, knowledge, and outputs. It adjusts the model's weights to perform better on your particular use cases.

Fine-Tuning Process

How fine-tuning works

Every feature designed to help your team work smarter with AI.

01

Training data preparation

Curate high-quality examples of the inputs and outputs you want the model to produce for your specific use cases.

02

Model training

Train the base model on your dataset, adjusting its weights to align with your desired behavior and output style.

03

Evaluation and testing

Assess the fine-tuned model against benchmarks to ensure it performs better than the base model on your tasks.

04

Iterative refinement

Improve results through multiple rounds of data curation, training, and evaluation until performance meets your standards.

05

Data security

Ensure training data is handled securely and that sensitive information does not leak through the fine-tuned model's outputs.

06

Prompt optimization

Even fine-tuned models benefit from well-engineered prompts. Optimize prompts for your custom model to maximize performance.

Benefits

When fine-tuning makes sense

Consistent style and format requirements that prompt engineering alone cannot achieve
Domain-specific knowledge that needs to be embedded in the model's behavior
High-volume use cases where reduced prompt length saves significant token costs
Specialized tasks where base models underperform despite prompt optimization
Brand voice and terminology consistency across all AI-generated content
Regulatory or compliance requirements that demand specific model behaviors

FAQ

Frequently asked questions

Should I fine-tune or use better prompts?

Start with prompt engineering — it is faster, cheaper, and more flexible. Fine-tune only when prompt optimization has been exhausted and you need capabilities that prompting alone cannot provide. TeamPrompt helps you optimize prompts first.

How does TeamPrompt relate to fine-tuning?

TeamPrompt helps teams optimize prompts before and after fine-tuning. Good prompt engineering often eliminates the need for fine-tuning. When fine-tuning is necessary, TeamPrompt helps manage the prompts used with custom models.

Is fine-tuning expensive?

Fine-tuning costs vary by provider and model size, but it requires both compute costs and significant human effort for data preparation. Prompt engineering through TeamPrompt is typically much more cost-effective.

How it works

Three steps from install to full AI security coverage.

1

Install

Add the browser extension to Chrome, Edge, or Firefox — or use the built-in AI chat. No proxy or VPN needed.

2

Configure

Enable the compliance packs for your industry, set DLP rules, and add your team's prompts to the shared library.

3

Protected

Every AI interaction is scanned in real time. Sensitive data is blocked before it leaves the browser. Your team has a full audit trail.

Ready to secure your team's AI usage?

Drop your email and we'll get you set up with TeamPrompt.

Free for up to 3 members. No credit card required.