What you'll learn:
- You will learn how to increase the robustness of you LLM calls by implementing structured outputs, acing, caching and retries
- How to generate synthetic data to establish a baseline for your RAG system, even if your RAG system don't have users yet
- How to filter out redundant generated data
- How to make all your LLM calls faster AND cheaper using asynchronous Python and caching
- How to not be held back by OpenAI rate limits
Master Advanced Retrieval Augmented Generation (RAG) with Generative AI & LLM
Unlock 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.
Synthetic Data Generation: Create high-quality synthetic datasets to simulate real-world scenarios and refine your RAG system.
Advanced Troubleshooting: Master debugging techniques for async code and handle complex challenges like OpenAI rate limits.
Requirements:
A modern laptop with Python installed or access to Google Drive.
Experience as a software engineer (2+ years preferred).
Intermediate Python programming skills or ability to learn quickly.
Basic understanding of data science (precision, recall, pandas).
Access to a pro version of ChatGPT or equivalent LLM tools.
Who Should Enroll:
Software engineers with experience in basic RAG implementations who want to advance their skills.
Data scientists and AI professionals looking to optimize their LLM-based systems.
Developers interested in mastering the latest RAG techniques for robust, scalable AI solutions.
Join this course today and transform your AI systems with the latest Advanced RAG techniques!
