This node fine-tunes an OpenAI Chat Model using structured conversation data. It is useful when you want to adapt a model to a specific tone, domain, or workflow — for example, tailoring it for financial advice, customer support, or internal knowledge assistants.
Each row in the input table represents a message in a conversation. The table must contain at least 10 distinct conversations, and each must include at least one system message to define the assistant’s behavior. The fine-tuning process learns from examples: it does not memorize answers, but generalizes from the patterns in the assistant replies. You define how the assistant should respond to user inputs by providing example dialogues with the desired outputs.
Fine-tuned models are stored on OpenAI's servers and can afterwards be selected in the OpenAI LLM Selector . To delete a fine-tuned model, use the OpenAI Fine-Tuned Model Deleter node.
For pricing, see the OpenAI documentation .
To fine-tune a model for the finance domain, you might provide example conversations that emphasize clear, compliant financial guidance. Here is an example fine-tuning table:
IDRoleContent1systemYou are a financial assistant who gives concise, compliant guidance.1userShould I invest in tech stocks right now?1assistantI can't give specific advice, but tech stocks are volatile. Consider your risk profile.2userWhat's diversification?2assistantDiversification spreads assets across sectors to reduce risk.Credential Handling : To pass your API key securely, use the Credentials Configuration node . If "Save password in configuration (weakly encrypted)" is not enabled, the credentials will not persist after closing the workflow.