Advanced Retrieval Augmented Generation

About this course

Master Advanced Retrieval Augmented Generation (RAG) with Generative AI & LLMUnlock the Power of Advanced RAG Techniques for Robust, Efficient, and Scalable AI Systems

Course Overview:

Dive deep into the cutting-edge world of Retrieval Augmented Generation (RAG) with this comprehensive course, meticulously designed to equip you with the skills to enhance your Large Language Model (LLM) implementations. Whether you're looking to optimize your LLM calls, generate synthetic datasets, or overcome common challenges like rate limits and redundant data, this course has you covered.

What You'll Learn:

• Implement structured outputs to enhance the robustness of your LLM calls.
• Master asynchronous Python to make your LLM calls faster and more cost-effective.
• Generate synthetic data to establish a strong baseline for your RAG system, even without active users.
• Filter out redundant generated data to improve system efficiency.
• Overcome OpenAI rate limits by leveraging caching, tracing, and retry mechanisms.
• Combine caching, tracing, and retrying techniques for optimal performance.
• Secure your API keys and streamline your development process using best practices.
• Apply advanced agentic patterns to build resilient and adaptive AI systems.

Course Content:

Introduction to RAG and Structured Outputs: Gain a solid foundation in RAG concepts and learn the importance of structured outputs for agentic patterns.

Setup and Configuration: Step-by-step guidance on setting up your development environment with Docker, Python, and essential tools.

Asynchronous Execution & Caching: Learn to execute multiple LLM calls concurrently and implement caching strategies to save time and resources.

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