Utilizing LLM Agents for Efficient Requirement Analysis and Specification
By Brain Aboze Elizabeth Ogunyemi
Data ScientistAbstract:
In this tutorial, we will build a practical application using Streamlit, leveraging open-source LLMs, embedding models, and vector stores—all implemented in Python. Attendees will learn how to integrate these components to create a user-friendly interface that enables seamless interaction with LLM agents. The session will cover the iterative development process, highlight the role of human evaluators in assessing output quality, and demonstrate the creation of efficient query engines for summarizing and semantically searching meeting notes. Additionally, attendees will gain a deep understanding of LLM agents, exploring various tools, tasks, and prompts accessible to the agents to enhance their functionality and effectiveness.
GO BACK
Other Talks
-
-
Automating Background Jobs with Celery: Making Your App Run Better
by Joseph Adediji -
Serving Machine Learning Models in Django with Fast API
by Wesley Kambale -
Keynote
by Samson Goddy -
Building Advanced Assistants (Agentic RAGs) with Gemma 2
by Ngesa Owiny
