{"id":3811,"date":"2026-08-10T09:07:53","date_gmt":"2026-08-10T09:07:53","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-gen-ai-rag-application-development-using-langchain-2024-5\/"},"modified":"2026-08-10T09:07:53","modified_gmt":"2026-08-10T09:07:53","slug":"udemy-gen-ai-rag-application-development-using-langchain-2024-5","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-gen-ai-rag-application-development-using-langchain-2024-5\/","title":{"rendered":"Udemy \u2013 Gen AI \u2013 RAG Application Development using LangChain 2024-5"},"content":{"rendered":"
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Descriptions<\/h2>\n

Gen AI \u2013 RAG Application Development using LangChain, This course uses Open AI GPT APIs, LangChain LLM Framework and Vector Databases and is intended to help you learn Langchain and build solid conceptual and hand-on proficiency to be able to develop RAG applications and projects. This course covers all the basics aspects to learn LLM and Frameworks like Agents, Tools, Chains, Retrievers, Output Parsers, Loaders and Splitters and so on in a very thorough manner with enough hands-on coding. It also takes a deep dive into concepts of Language Embeddings and Vector Databases to help you develop efficient semantic search and semantic similarity based RAG Applications. We will also cover multiple Prompt Engineering techniques that will make your RAG Applications more efficient. Observability including Traces and Evaluations using LangSmith are also covered as part of this course. Create a comprehensive HR Chatbot that is able to respond with answers from a HR Policy and Procedure database loaded into a Vector DB, and retain conversational memory like ChatGPT. Build UI using Streamlit.<\/p>\n

What you\u2019ll learn<\/h3>\n