Description:

In 2024, all of us use Large Language Models with the help of chat interfaces such as chatgpt, claude.ai and others. We use these models for complex use cases such as code generation, knowledge management and content generation. But for enterprise level applications, these often lack factual grounding and domain specificity. Retrieval-Augmented Generation (RAG) is the future - enhancing language models by allowing them to retrieve relevant information from large knowledge bases during generation.

In this talk, we'll explore how to unleash the power of RAG for creating cutting-edge enterprise apps. You'll learn practical tips for curating domain-specific corpora, fine-tuning RAG models on proprietary data, optimizing for low-latency retrieval, and maintaining secure, scalable RAG pipelines.

Using an industry specific use case, we'll dive into methods for query answering over internal docs, enriching CRMs with external data, and building domain-specific dialogue assistants.

Don't miss this chance to get hands-on with RAG using open-source libraries such as huggingface, llamaindex etc. along with code samples. Whether you're enhancing search engines, automating workflows, or exploring the latest in knowledge-grounded language AI - RAG is a game-changer you can't afford to ignore.

Get ready to build the future of enterprise AI apps with retrieval-augmented generation!

Prerequisites:

Familiarity with basic machine learning and natural language processing concepts. Some experience with Python and deep learning libraries like PyTorch or TensorFlow would be helpful but not strictly required.

Content URLs:

code: https://github.com/infinite-Joy/webinars/tree/main/creating_gpt_chatbots_for_enterprise_usecases

ppt: https://github.com/infinite-Joy/webinars/blob/main/creating_gpt_chatbots_for_enterprise_usecases/Create%20RAG%20apps%20for%20Enterprise%20Use%20Cases.pdf

Speaker Info:

Joydeep Bhattacharjee is an accomplished Applied Research Scientist and Staff Software Engineer specializing in AI/ML applications. Currently, he is working at a research group at Samsung SSIR focused on leveraging cutting-edge deep learning and AI to improve semiconductor manufacturing yields and fab processes.

Joydeep is also author of two books on applied machine learning and has worked with open communities such as HuggingFace to create open-source models for language, audio etc.

Speaker Links:

• LinkedIn: https://www.linkedin.com/in/joydeep-bhattacharjee-934a1157/
  • Youtube: https://www.youtube.com/channel/UCkgbnb9ibABSL5X9Au-8mIA
  • [Book] Practical Machine Learning with Rust: https://www.amazon.in/Practical-Machine-Learning-Rust-Applications/dp/1484251202
  • [Book] fastText Guide: https://www.amazon.in/fastText-Quick-Start-Guide-representation/dp/1789130999
  • Github: https://github.com/infinite-Joy
  • HuggingFace: https://huggingface.co/infinitejoy
  • Twitter: https://twitter.com/alt227Joydeep