GenAI's Act II and Vector Databases (Vol. 9)
Sequoia’s “Generative AI’s Act II ” is like a State of the Union address for GenAI. The esteemed investor revisits past prognostications and refreshes their view of how the market will unfold.
Three ideas leap out that point to the evolution of vector databases.
Next up for GenAI: help solve human problems from end-to-end
Sequoia describes “Act I” as the technology-out wave of novelty apps—lightweight demonstrations of cool new technology.?Act II, says Sequoia, is where “GenAI will shift towards solving genuine human problems with enhanced editing interfaces, superior outputs, and more diverse needs.”
What are these genuine human problems? Their “Generative AI Market Map V3” contains 31 categories and companies:
Explore their market map here or in the original article .
Act II’s applications will spark changes in systems architecture
These applications, says Sequoia, will spark the rise of a new GenAI systems architecture, which includes the vector database and the specialization of task or domain-specific foundation models and apps.
Another Silicon Valley visionary firm, Andreessen Horowitz, pronounced vector databases “the most important piece of a generative AI architecture” (a claim that should come with the disclosure that Andreessen Horowitz has invested over $100 million in a vector database company).
Sequoia makes no such proclamation of the primacy of vector databases, but Sequoia and Andreessen Horowitz agree that vector databases play a prominent role in the Generative AI Infrastructure stack.
领英推荐
Why Act II GenAI apps need a vector database
A challenge with LLMs, Andreessen Horowitz says , is that they hallucinate answers that seem confident and factually correct. For example, “Asking an LLM for the gross margin of Apple last quarter can result in a confident answer of $63 billion, which models can back up by explaining that they subtracted $25 billion in cost of goods from $95 billion in revenue. But that’s wrong in three ways,”
This is where vector databases come in. They help developers store relevant contextual data for LLM apps. Instead of sending unstructured documents with every API call, developers store data in a vector database and pick the most relevant and highest-quality data sources for any given query — an approach called in-context learning.
Vector databases store data in semantically meaningful embeddings, which pre-process and offload operations such as calculating gross margins to the database, which can be recombined with LLM output for confident-sounding and accurate responses.
Vector databases also help applications access up-to-date data, potentially in real time. We’ve written about how real-time vector databases do this for “Act II” applications like:
Vector databases complement LLMs with up-to-date, accurate, contextual insight that, combined with prompt interfaces and natural language, promise to help usher in the next act for GenAI —solving end-to-end human problems.
Read More About GenAI, Act II
Read Generative AI’s Act Two from Sequoia.
Read Emerging Architectures for LLM Applications from Andreessen Horowitz
For a detailed case study on time series vector databases in clinical trials, read Time Series Vector Databases in Healthcare case study by Syneos .
Read about vector databases and prompting in {The, Weekly, Vector} newsletter #7: The Implications of Prompt Interfaces (Vol. 7) .