Snowflake's AI_EXTRACT function (powered by arctic-extract) handles zero-shot document extraction well, but domain-specific documents with unusual layouts can reduce accuracy. Fine-tuning arctic-extract lets you adapt the model to your specific documents using labeled examples — all managed within Snowflake via SQL. The tutorial walks through preparing labeled training data, creating versioned Snowflake Datasets, launching a fine-tuning job with SNOWFLAKE.CORTEX.FINETUNE, monitoring progress, running inference with the custom model, storing results, iterating on the model, and promoting it to production. Key guidance includes starting with at least 20 labeled documents, including null responses for absent fields, tuning epoch count based on dataset size, and using dataset versioning for reproducible iterations. Fine-tuning is recommended when zero-shot accuracy falls short and prompt tuning has been exhausted.
Table of contents
The Problem: Domain Drift in Document ExtractionWhat You’ll BuildHow Fine-Tuning Arctic-Extract WorksStep 1: Prepare Your Documents and LabelsStep 2: Build the Training TableStep 3: Create a Snowflake DatasetStep 4: Launch the Fine-Tuning JobOptional ParametersGet Neeraj Jain’s stories in your inboxStep 5: Monitor Job ProgressStep 6: Run Inference with Your Fine-Tuned ModelStep 7: Store the Results in a TableStep 8: Iterating on Your ModelStep 9: Model Promotion (Deployment in Production)Sort: