Vibe Coding in Practice: Motivations, Challenges, and a Future Outlook – a Grey Literature Review
Abstract
Vibe coding is the practice where coders rely on AI code generation tools through intuition and trial-and-error without necessarily understanding the underlying code. Despite widespread adoption, there has been no systematic investigation into why vibe coders engage in vibe coding, what they experience while doing so, and how they approach quality assurance (QA) and perceive the quality of AI-generated code. To this end, we conduct a systematic grey literature review of 101 practitioner sources, extracting 518 firsthand behavioral accounts about vibe coding practices, challenges, and limitations. Our analysis reveals a speed–quality trade-off paradox, where vibe coders report being motivated by speed and accessibility, often experiencing rapid “instant success and flow”, yet most perceive the resulting code as fast but flawed. Our analysis suggests that QA practices are frequently overlooked, with many reporting skipping testing, relying on the models’ or tools’ outputs without modification, or delegating checks back to the AI code generation tools. While vibe coding lowers barriers and accelerates prototyping, it can introduce reliability and maintainability concerns. These insights carry implications for tool designers and software development teams. Understanding how vibe coding is practiced today is crucial for guiding its responsible use and preventing a broader QA crisis in AI-assisted development.
Read the paper
Related studies
- Regulation of nucleolus assembly by non-coding RNA polymerase II transcripts
- For a Healthy Histone Code, a Little SUMO in the Tail Keeps the Acetyl Away
- Discovering a binary CTCF code with a little help from BORIS
- Article Commentary: Stabilizing the Code–-Methods to Preserve RNA Prove Their Worth
- Foreword to the Special Issue for the RINTC (The Implicit Seismic Risk of Code-Conforming Structures) Project
- Commentary: Promoting Health and Well-being in Pediatric Primary Care Settings: Using Health and Behavior Codes at Routine Well-child Visits
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.