Room 5 

10:20 - 11:20 


Talk (60 min)

Beyond Vectors: Evolving GenAI through Transformative Tools and Methods

Embark on a thought-provoking exploration of GenAI's evolution with "Beyond Vectors: Evolving GenAI through Transformative Tools and Methods."

Machine Learning

Tailored for engineers seeking fresh perspectives, this session encourages practitioners to step beyond familiar Vector Database practices. It's not just a departure; it's a pragmatic leap forward into precision methodologies for data quality and crafting datasets essential for Retrieval-Augmented Generation (RAG) excellence. We'll navigate the complexities of adding structural context through graph databases, shedding light on the nuanced limitations of distance metrics like Cosine Similarity. Join us for this insightful journey, pushing the boundaries of GenAI evolution with transformative tools and methods.

Key Themes:

Methodical Precision in Data Quality and Dataset Construction for RAG Excellence: Uncover an integrated methodology for refining, curating, and constructing datasets that form the bedrock of transformative GenAI applications. Specifically, focus on the six key aspects crucial for Retrieval-Augmented Generation (RAG) excellence.

Navigating Structural Context with Awareness: Explore the infusion of structural context through graph databases while understanding the nuanced limitations of the Cosine Similarity distance metric. Recognize its constraints in certain contexts and the importance of informed selection in the quest for enhanced data richness.

The Logging Imperative: Recognize the strategic significance of logging in the GenAI landscape. From application health to profound business insights, discover how meticulous logging practices unlock valuable information and contribute to strategic decision-making.

Key Takeaways:

- Master a methodical approach to ensuring data quality and constructing datasets specifically tailored for Retrieval-Augmented Generation (RAG) excellence.
- Navigate the complexities of adding structural context, including an awareness of limitations in distance metrics like Cosine Similarity.
- Understand the strategic significance of logging for application health and insightful business analytics.

Join us on this methodologically rich exploration, "Beyond Vectors," engineered to take your GenAI practices beyond the current Vector Database norms, unlocking a new frontier in GenAI evolution with transformative tools and methods!

Alison Cossette

Alison Cossette is a dynamic Data Science Strategist, Educator, and Podcast Host. As a Developer Advocate at Neo4j specializing in Graph Data Science, she brings a wealth of expertise to the field. With her strong technical background and exceptional communication skills, Alison bridges the gap between complex data science concepts and practical applications.

Alison’s passion for responsible AI shines through in her work. She actively promotes ethical and transparent AI practices and believes in the transformative potential of responsible AI for industries and society. Through her engagements with industry professionals, policymakers, and the public, she advocates for the responsible development and deployment of AI technologies. She is currently a Volunteer Member of the US Department of Commerce - National Institute of Standards and Technology's Generative AI Public Working Group Alison’s academic journey includes Masters of Science in Data Science studies, specializing in Artificial Intelligence, at Northwestern University and research with Stanford University Human-Computer Interaction Crowd Research Collective. Alison combines academic knowledge with real-world experience. She leverages this expertise to educate and empower individuals and organizations in the field of data science.

Overall, Alison Cossette’s multifaceted background, commitment to responsible AI, and expertise in data science make her a respected figure in the field. Through her role as a Developer Advocate at Neo4j and her podcast, she continues to drive innovation, education, and responsible practices in the exciting realm of data science and AI.