From the source
Today, we're sharing a deep dive into the composite model architecture behind those models.
They combine specialized knowledge from retrieval-augmented generation (RAG), reasoning from state-of-the-art large language models (LLMs), and error fixing from a custom streaming post-processing model.
While this may sound complex, it enables v0 to achieve significantly higher quality when generating code.
Further, as base models improve, we can quickly upgrade to the latest frontier model while keeping the rest of the architecture stable.
Copy link to heading Why does v0 need a composite model architecture?
Most AI models¹ are one of two things: a proprietary model from a frontier model provider or an open-source model from a third-party host.
While building AI products like v0 and tools like the AI SDK, we’ve found limitations with both of these approaches: Model knowledge can quickly become outdated for topics that change fast Frontier model labs don’t have the resources or desire to focus on specific use cases like building web apps …
