AfterVibe: What Remains When the Conversation Ends

Modern software development is increasingly defined by vibe coding—a practice where developers rely on AI assistants to generate code through conversational prompts. While this accelerates development, it creates a significant quality assurance problem: the developer's intent is often lost in an ephemeral chat transcript, and the resulting code is frequently opaque. Traditional code review is struggling to keep pace with the volume and complexity of this AI-generated output, leaving a gap where no authoritative document exists to explain what the code is supposed to do.

AfterVibe addresses this by distilling the ephemeral conversation and the resulting code artifact into a structured, durable natural-language specification. The framework operates on the principle that requirements are implicit in the developer's actions during a coding session. By analyzing the conversation trajectory and the final code diff, AfterVibe extracts a specification consisting of three parts: intent and rationale, essential design decisions, and undiscoverable facts (such as domain-specific thresholds or contracts).

To ensure these specifications are accurate, AfterVibe uses a regeneration test. A blind AI agent—one with no access to the original conversation or code—is tasked with re-implementing the artifact using only the distilled specification. The resulting code is then evaluated by a three-tier verification pipeline that checks for functional equivalence through test execution, verification conditions, and ground-truth alignment. If the regeneration fails, the specification is iteratively refined until it is strong enough to produce passing code.

AfterVibe demonstrates that specifications can be recovered as a byproduct of the coding process, rather than requiring separate, tedious authoring effort. By elevating these specifications to first-class artifacts, AfterVibe enables a new review paradigm where humans can inspect abstract, readable statements of intent instead of voluminous, machine-generated code. This approach not only makes AI-generated code reviewable but also establishes a source of record that can survive long after the original conversation has ended.