Advanced Retrieval Augmented Generation

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Free Download Advanced Retrieval Augmented Generation
Published 8/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 5h 27m | Size: 2.2 GB
How to make Advanced RAG work in practice with Evaluations, Agentic Patterns and Generative AI with LLM

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
Requirements
Have Docker installed on your machine
Access to a modern powerful laptop with python installed or a Google Drive account
Working experience as a Software Engineer, preferrably more than two years
At least intermediate Python Programming or the ability to learn it fast (eg: Seniority in another Programming Language)
Willing to spend about ten dollars for running the LLM calls (either locally or through OpenAI)
Access to pro version of ChatGPT (or equivalent)
Basic of data science (precision, recall, pandas)
Ability to debug by yourself, especially typos (we will use async code, you must be comfortable reading tracebacks)
You know what RAG means and have already implemented Basic or Naive RAG in a tutorial, at least
Description
Master Advanced Retrieval Augmented Generation (RAG) with Generative AI & LLMUnlock the Power of Advanced RAG Techniques for Robust, Efficient, and Scalable AI SystemsCourse Overviewive 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!
Who this course is for
Software Engineer with at least 2 years of Experience
Beginners can follow the video but won't be able to replicate the practical part (They can still learn a lot)
Data Scientists / Analysts with at least 2 years of Experience
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2.21 GB | 00:30:36 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

1 - 000 INTRO post processed mp4 (120.75 MB)
2 - 00 SETUP 01 OPENAI API KEY mp4 (34.83 MB)
3 - 00 SETUP 02 INSTALL VIRTUAL ENV JUPYTER mp4 (16.51 MB)
4 - 00 SETUP 03 DOCKER LANGFUSE mp4 (52.32 MB)
5 - 00 SETUP 04 CREATE REPO FOR OUR CODE mp4 (27.91 MB)
1 - 01 ROBUSTNESS 01 PROTECT API KEY WITH DOTENV mp4 (61.52 MB)
10 - fname 01 ROBUSTNESS 10 Update Wrappertscproj mp4 (43.94 MB)
11 - 01 ROBUSTNESS 11 TRACING LANGFUSE mp4 (217.32 MB)
12 - 01 ROBUSTNESS 12 Tracing+Caching mp4 (51.04 MB)
13 - 01 ROBUSTNESS 13 Retrying Decorator mp4 (129.73 MB)
14 - 01 ROBUSTNESS 14 Demo Retry and putting it all together mp4 (119.97 MB)
15 - 01 ROBUSTNESS 15 theory structured outputs agentic patterns post processed mp4 (56.18 MB)
16 - 01 ROBUSTNESS 16 STRUCTURED OUTPUTS DEMO post processed mp4 (135.69 MB)
17 - 01 ROBUSTNESS 17 Robust Structured Outputs post processed mp4 (219.83 MB)
18 - 01 ROBUSTNESS 18 Section Conclusion post processed mp4 (20.15 MB)
2 - 01 ROBUSTNESS 02 CALLING AN OPENAI LLM mp4 (128.49 MB)
3 - 01 ROBUSTNESS 03 THEORY TOKENS mp4 (27.87 MB)
4 - 01 ROBUSTNESS 04 THEORY LLM mp4 (23.51 MB)
5 - 01 ROBUSTNESS 04 ASYNCHRONOUS CODE mp4 (144.93 MB)
6 - 01 ROBUSTNESS 06 THE FIVE PROBLEMS WE NEED TO SOLVE (1) mp4 (80.68 MB)
7 - 01 ROBUSTNESS 07 Caching DISKCACHE mp4 (94.14 MB)
8 - 01 ROBUSTNESS 08 Caching LLM Calls Cached mp4 (228.81 MB)
9 - 01 ROBUSTNESS 09 ignore cache dir mp4 (12.7 MB)
1 - 02 RETRIEVAL 01 SECTION INTRODUCTION post processed mp4 (75.77 MB)
1 - 02 RETRIEVAL 02 DATASET INTRODUCTION post processed mp4 (56.64 MB)
2 - 02 RETRIEVAL 03 LOAD DATASET post processed mp4 (37.06 MB)
3 - 02 RETRIEVAL 04 HOW TO GENERATE post processed mp4 (45.48 MB)

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