Tabnine Guide

Get the most from generative AI through personalization

Generative AI can deliver highly relevant results for any engineering team through context, connection, coaching, and customization. Learn how you can get personalized AI results unique to your company and software development teams without sacrificing your privacy or security.

What's inside

Read about the four levels of progressive personalization Tabnine uses to optimize the performance of your AI code assistant
Dive into how retrieval-augmented generation (RAG) and RAG with semantic memory create local and global code awareness for Tabnine
Explore how fine-tuning and low-rank adaptation (LoRA) are used to create custom Tabnine AI models
Discover the best use cases and deployment options for a fine-tuned AI model

Download the guide