Ben Needs a Friend – An intro to building Large Language Model applications


People say it’s difficult to make friends after college, impossible after grad school and just generally to give up after 30. Approaching 40 - I’ve decided to take matters into my own hands. Rather than go outside and meet people, I’ve decided, like many top-tier companies, to replace all that manual work with AI. In this tutorial, I’ll show you how to make your own AI friend, powered by Large Language Models (LLM).

Along the way, we’ll cover some of the essential topics in LLM development. Our first step will be adjusting our new friend to our preferences based on prompt engineering and fine-tuning. Then, we will develop a “history” of our friendship using document embeddings and enable our friend to discuss that history (Retrieval-Augmented Generation). Finally, we will provide our friend with the tools it needs to be able to invite us to interesting local events. We’ll use the LangChain and transformers libraries to explore the pros and cons of different open and closed-source implementations in terms of cost and performance. The methods we’ll be using can be hosted locally and are either free or have minimal cost (e.g. OpenAI APIs).

By the end of the tutorial, participants will have a basic familiarity with how to use the latest tools for LLM development and, for anything they’re not clear on, they can always ask their new AI friend for advice. We’ll conclude with a discussion of what our friend can and cannot do and why it may be better to just go outside more.


Ben is a Data Science consultant working with multiple sectors on building AI strategies and applications. Previously, he led Data Science teams in academia (Northeastern University, MIT) and industry (ThriveHive). He obtained his Masters in Public Health (MPH) from Johns Hopkins and his PhD in Policy Analysis from the Pardee RAND Graduate School. Since 2014, he has been working in data science for government, academia and industry. His major focus has been on Natural Language Processing (NLP) technology and applications. Throughout his career, he has pursued opportunities to contribute to the larger data science community. He has presented his work at conferences, published articles, taught courses in data science and NLP, and is co-organizer of the Boston chapter of PyData. He also contributes to volunteer projects applying data science tools for public good.

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