Unlocking the power of unstructured data with RAG
Unstructured data holds valuable information about codebases, organizational best practices, and customer feedback. Here are some ways you can leverage it with RAG, or retrieval-augmented generation.
How AI enhances static application security testing (SAST)
Here’s how SAST tools combine generative AI with code scanning to help you deliver features faster and keep vulnerabilities out of code.
What is retrieval-augmented generation, and what does it do for generative AI?
Here’s how retrieval-augmented generation, or RAG, uses a variety of data sources to keep AI models fresh with up-to-date information and organizational knowledge.
Customizing and fine-tuning LLMs: What you need to know
Learn how your organization can customize its LLM-based solution through retrieval augmented generation and fine-tuning.
The architecture of SAST tools: An explainer for developers
More developers will have to fix security issues in the age of shifting left. Here, we break down how SAST tools can help them find and address vulnerabilities.
The architecture of today’s LLM applications
Here’s everything you need to know to build your first LLM app and problem spaces you can start exploring today.
A guide to designing and shipping AI developer tools
GitHub’s design experts share 10 tips and lessons for designing magical user experiences for AI applications and AI coding tools.
