Ben Greenberg

How to Decipher User Uncertainty with GenAI and Vector Search

What if your search could understand what users mean, not just what they type? See how vector search and GenAI solve the critical problem of user uncertainty.

How to Decipher User Uncertainty with GenAI and Vector Search
#1about 4 minutes

Why traditional search fails with ambiguous data and queries

Both vague user search queries and poorly structured source data create ambiguity that traditional keyword-based systems cannot effectively resolve.

#2about 5 minutes

Understanding vector embeddings and measuring semantic closeness

Vector embeddings represent data as numerical lists, enabling the measurement of conceptual closeness using mathematical formulas like Euclidean and cosine distance.

#3about 4 minutes

How embedding models capture context and relationships

Embedding models like GPT use transformer layers and neural network principles to analyze input and generate vector embeddings that capture semantic meaning.

#4about 5 minutes

Vector search as the memory layer for RAG and Agentic AI

Vector search provides the essential memory component for both Retrieval-Augmented Generation (RAG) and Agentic AI, which also require tools and planning capabilities.

#5about 3 minutes

The risks of centralized control over AI models

Centralized, closed-source control over how embedding models are trained and weighted poses a significant risk to the future of information and understanding.

#6about 3 minutes

Exploring open source and decentralized AI alternatives

Decentralized and open-source platforms for AI compute and model training offer an alternative to closed systems, preserving user autonomy and control.

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How E.On productionizes its AI model & Implementation of Secure Generative AI.

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Practical use cases for vector embeddings
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Enter the Brave New World of GenAI with Vector Search

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