Level Up Your AI Agents with Fine-Tuning (n8n)
AI Summary
This video explains how to use fine-tuning to create specialized, reliable AI agents aligned with your desired tone of voice. Fine-tuning differs from prompt engineering and retrieval-augmented generation (RAG) systems by focusing on influencing AI response style, tone, and format rather than teaching new information.
The video covers getting started quickly using simple tools like Google Sheets for training data and OpenAI’s playground and API to create fine-tuned models based on base models like GPT-4.1 Mini. It highlights costs, efficiency, use cases, and different fine-tuning strategies such as supervised fine-tuning and parameter-efficient fine-tuning.
The presenter demonstrates building a scaleable fine-tuning system integrating AirTable and N8N workflows that automate dataset management, uploading training data, triggering fine-tuning jobs, and monitoring their status. The video also explores practical uses including matching writing style, enforcing industry-specific terminology, high-volume workflow optimization, and combining fine-tuned models with RAG vector stores for accurate dynamic responses.
Best practices for preparing training data, example counts, and prompt consistency are discussed, along with the limitations and considerations of using fine-tuning directly within AI agents. The creator encourages focusing on prompt engineering and RAG techniques before fine-tuning for robust AI solutions.
Viewers are invited to join a community for access to blueprints, discussions, and workshops related to these concepts.