How we made v0 an effective coding agent

Summary

Last year we introduced the v0 Composite Model Family, and described how the v0 models operate inside a multi-step agentic pipeline. Three parts of that pipeline have had the greatest impact on reliability. These are the dynamic system prompt, a streaming manipulation layer that we call “LLM Suspense”, and a set of deterministic and model-driven autofixers that run after (or while!) the model finishes streaming its response.What we optimize forThe primary metric we optimize for is the percentage of successful generations. A successful generation is one that produces a working website in v0’s preview instead of an error or blank screen. But the problem is that LLMs running in isolation encounter various issues when generating code at scale. In our experience, code generated by LLMs can have errors as often as 10% of the time. Our composite pipeline is able to detect and fix many of these errors in real time as the LLM streams the output. This can lead to a double-digit increase in success rates.Your product’s moat cannot be your system prompt. However, that does not change the fact that the system prompt is your most powerful tool for steering the model.For example, take AI SDK usage. AI SDK ships major and minor releases regularly. Models often rely on outdated internal knowledge (their “training cutoff”), but we want v0 to use the latest version. This can lead to errors like using APIs from an older version of the SDK. These errors directly reduce our success rate.Many agents rely on web search tools for ingesting new information. Web search is great (v0 uses it too), but it has its faults. You may get back old search results, like outdated blog posts and documentation. Further, many agents have a smaller model summarize the results of web search, which in turn becomes a bad game of telephone between the small model and parent model. The small model may hallucinate, misquote something, or omit important information.Instead of relying on web search, we detect AI-rel...