Retrieval Augmented Generation - RAG (training)

Colleagues, in the “Retrieval Augmented Generation” program from DeepLearning.AI you will learn to design and build RAG systems tailored to real-world needs, weigh tradeoffs between cost, speed, and quality to choose the right techniques for each component of a RAG system, and a foundational framework to adapt RAG systems as new tools and methods emerge. Gain high-demand skills in Secure Coding, Security Controls, Large Language Modeling, Information Management, Natural Language Processing, Sampling (Statistics), System Monitoring, Artificial Intelligence, Data Security, MLOps (Machine Learning Operations), Metadata Management, Application Performance Management, Prompt Engineering, Continuous Monitoring, Algorithms, and Generative AI. Learn how to build RAG systems that connect LLMs to external data sources. You’ll explore core components like retrievers, vector databases, and language models, and apply key techniques at both the component and system level. Through hands-on work with real production tools, you’ll gain the skills to design, refine, and evaluate reliable RAG pipelines - and adapt to new methods as the field advances. Skill-based training modules include: 1) RAG Overview, 2) Information Retrieval with Vector Databases, 3) LLMs and Text Generation, and 4) RAG Systems in Production. Through hands-on labs, you will: Build your first RAG system by writing retrieval and prompt augmentation functions and passing structured input into an LLM, Implement and compare retrieval methods like semantic search, BM25, and Reciprocal Rank Fusion to see how each impacts LLM responses, Scale your RAG system using Weaviate and a real news dataset—chunking, indexing, and retrieving documents with a vector database, Develop a domain-specific chatbot for a fictional clothing store that answers FAQs and provides product suggestions based on a custom dataset, Improve chatbot reliability by handling real-world challenges like dynamic pricing and logging user interactions for monitoring and debugging, and Develop a domain-specific chatbot using open-source LLMs hosted by Together AI for a fictional clothing store that answers FAQs and provides product suggestions based on a custom dataset.

Enroll today (teams & execs welcome): imp.i384100.net/550N6j 

For your listening-reading pleasure consider:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 -  “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) or (Kindle) 

3 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) or (Kindle) 

Much career success, Lawrence E. Wilson - AI Academy (share with your team)