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Getting the most from GenAI through personalization
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Getting the most from GenAI through personalization

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Sharon Kruk /
2 minutes /
January 23, 2025

In the past few years, AI coding assistants have gone from a “nice to have” for savvy individual developers to a “must have,” being implemented by engineering managers across their teams to increase developer productivity, efficiency, and satisfaction. Early results from adopting AI software development tools have been promising — but there’s still significant room for improvement, especially when it comes to the quality of the responses in the context of what the user is asking.

The large language models (LLMs) behind the majority of AI coding assistants have inherent limitations. By design, these LLMs are universal; although they’ve been trained on vast amounts of data and contain billions of parameters, they’re not generally aware of the specific code and distinctive patterns of an individual organization.

This lack of context and domain-specific knowledge likens their recommendations more to a skilled software engineer off the street rather than a deeply experienced engineer who’s familiar with how an organization works. The result is that the recommendations from AI code assistants, while accurate, often aren’t specifically tailored to an individual developer’s needs.

Imagine a new developer starting to write code in your organization without any onboarding. They may be very skilled, but without appropriate onboarding, they wouldn’t be aligned with the organization’s architectural requirements, existing microservices, best practices, coding styles, etc.

Our Personalizing AI coding assistants to your organization guide explores how to effectively “onboard” Tabnine into your organization:

  • 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) can be used to create custom Tabnine AI models.
  • Discover the best use cases and deployment options for a fine-tuned AI model.

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.

Get the guide to learn more.