Semantic email
Send email to fill Google Forms using AI
Semantic document
Auto-fill Google Forms from documents
Finetuning
Build your own AI model using data in Google Sheets
All products
Extract structured data from customer conversations
Customize UI
Change layout, hide fields & redirect on submit
Embed in website
Embed Google Forms in your website
Assign points
Assign different points for each answer & calculate score
File upload
Upload files in Google Forms without login
Email notification
Email Google Forms response to your users & co-workers
Enhance Google Forms into CRM
Form signature
Collect signature in Google Forms
Document signature
Collect signature in Google Docs
Sheet signature
Collect signature in Google Sheets
Signature workflow
Collect multiple signatures in Google Forms
Add legal & HIPAA compliance to Google Forms
Intake form
Create intake forms that accepts eSignature from patients
HIPAA form
Mask PHI fields in email & links for HIPAA compliance
Health & Safety
Create health & safety forms using Google Forms
Meal Prep Software
Meal prep software for weekly changing menu
Online Canteen
Take canteen orders for weekly changing menu
Order form
Calculate order amount in Google Forms
WhatsApp form
Take online orders from your WhatsApp contacts
Payment form
Accept payment in Google Forms
Website builder
Organize your forms like Linktree
Take food orders for frequently changing menu
Fine-tuning is the process of taking a pre-trained AI model and optimizing it for a specific task by training it further on a specialized dataset. Instead of training a model from scratch, fine-tuning allows us to leverage existing AI capabilities while tailoring the model to our unique needs.
Higher Accuracy: Pre-trained models are general-purpose. Fine-tuning makes them domain-specific, improving performance.
Less Data Required: Since the model already has foundational knowledge, we only need a smaller, high-quality dataset.
Better Efficiency: Fine-tuning can be significantly faster and cheaper than training a model from scratch.
Promptrepo simplifies fine-tuning by breaking it into the following steps:
Define Your Task – Classification, Extraction, or Generation
Prepare Training Data – Curate examples that demonstrate the desired behavior
Train the Model – Use Promptrepo to fine-tune on your dataset
Evaluate & Iterate – Test and improve the model based on real-world performance
Let’s say we want to classify food items into different categories such as Red Meat, Beverages, Cheese, or General Foods. Instead of building a classifier from scratch, we fine-tune an existing model with food-specific data.
Steps:
Collect Data: Create a spreadsheet with food names & their respective categories.
Fine-tune in Promptrepo: Upload the dataset and train the model.
Test & Deploy: The model can now classify new food items accurately.
Fine-tuning is a powerful way to build high-performing AI models with minimal effort. In the next articles, we’ll explore how to build classification, extraction, and generation models using Promptrepo.
Build a classification model
Build an extraction model
Build an generative model
Click Submit to finish.