Learn to build large language model applications: vector databases, langchain, fine tuning and prompt engineering. Learn more

Emerging Architectures for Large Language Model Applications


Generative Al and Large Language Models have taken the world by storm. Applications like Bard, ChatGPT, Midjourney, and Dall.E crossed the proverbial chasm of technology adoption lifecycle; some applications like content generation and summarization have entered the mainstream. A large number of enterprise use cases of LLM applications still remain challenging. There are inherent challenges for a lot of tasks that require a deeper understanding of trade-offs like latency, accuracy, and consistency of responses. Any serious application of LLMs requires an understanding of nuances in how LLMs work, embeddings, vector databases, and retrieval.

This introductory tutorial will introduce the audience to prevalent approaches to building a custom large language model application. We will present a canonical architecture for an LLM application and various available commercial and open-source tools and technologies available to build these applications.

No prior background in Generative AI or LLMs is necessary to attend this talk.

Instructor Raja Iqbal from Data Science Dojo, guiding participants through the LLM Bootcamp.
Raja Iqbal

CEO and Chief Data Scientist at Data Science Dojo

Raja Iqbal is a data scientist, a passionate educator, and an internationally recognized speaker on data science. Prior to Data Science Dojo, Raja worked at Microsoft in a variety of research and development roles involving machine learning and data mining at very large scale.


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