Vibe coding keeps track of project context only within the boundaries of what you provide during the interaction. The model does not retain long-term memory of your project, so it cannot automatically remember previous implementations, naming conventions, or architectural decisions unless you supply them explicitly. Developers work around this by regularly including file summaries, directory structures, or important class definitions in their prompts. This gives the model enough reference points to maintain consistency across files and sessions.
Over time, teams often build “prompt scaffolds” containing key structural details. These scaffolds might list important modules, core class names, architectural patterns, and key constraints such as vector dimensions for Milvus indexing or the specific search parameters used throughout the project. Whenever developers want to generate or modify code, they paste this scaffold along with the specific section they want to change. This method ensures the model stays aligned with project reality and avoids inconsistencies like mismatched imports or renamed variables.
Another effective technique is to ask the model to generate summaries of its own output and reuse those summaries in future prompts. For complex features—such as vector search workflows where ingestion, indexing, and API routes span multiple modules—summaries help the model maintain coherence across sessions. You can also periodically ask the model to verify that new code remains consistent with older files by pasting multiple modules and requesting a “consistency check.” Ultimately, maintaining project context is a shared responsibility: vibe coding supports it, but developers must provide the crucial information needed for continuity.