AI Content Hub

Offers a wide range of courses on machine learning, deep learning, and AI, taught by experts like Andrew Ng. Excellent for building foundational and advanced skills.

A comprehensive resource for learning prompt engineering techniques, best practices, and common patterns for getting the most out of LLMs.

A collection of example code and guides for accomplishing common tasks with the OpenAI API, including best practices and optimization techniques.

Comprehensive documentation for Claude AI, including prompt engineering tips, safety guidelines, and API usage examples.

Retrieval Augmented Generation (RAG) combines the knowledge of LLMs with specific document retrieval. To build a basic RAG system: 1) Process your documents into embeddings, 2) Store them in a vector database, 3) When queried, retrieve relevant content based on similarity, 4) Augment your prompt with this content, and 5) Generate contextually informed responses.

Create reusable prompt templates: 1) Identify common prompt patterns in your application, 2) Extract variable parts into placeholders, 3) Create template functions with parameter validation, 4) Build a library of tested templates, 5) Implement version control for template changes.

A managed vector database service optimized for machine learning applications, perfect for building RAG systems and semantic search.

A Python framework for building interactive web applications for machine learning and data science projects with minimal code.

Create user interfaces for machine learning models with just a few lines of Python code. Great for demos and prototypes.

An open-source embedding database that makes it easy to build LLM applications with semantic search capabilities.