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Context: ChatGPT and other large language models (LLMs) are widely usedacross healthcare, business, economics, engineering, and software engineering(SE). Despite their popularity, concerns persist about their reliability,especially their error rates across domains and the software developmentlifecycle (SDLC).  Objective: This study synthesizes and quantifies ChatGPT's reported errorrates across major domains and SE tasks aligned with SDLC phases. It providesan evidence-based view of where ChatGPT excels, where it fails, and howreliability varies by task, domain, and model version (GPT-3.5, GPT-4,GPT-4-turbo, GPT-4o).  Method: A Multivocal Literature Review (MLR) was conducted, gathering datafrom academic studies, reports, benchmarks, and grey literature up to 2025.Factual, reasoning, coding, and interpretive errors were considered. Data weregrouped by domain and SE phase and visualized using boxplots to show errordistributions.  Results: Error rates vary across domains and versions. In healthcare, ratesranged from 8% to 83%. Business and economics saw error rates drop from ~50%with GPT-3.5 to 15-20% with GPT-4. Engineering tasks averaged 20-30%.Programming success reached 87.5%, though complex debugging still showed over50% errors. In SE, requirements and design phases showed lower error rates(~5-20%), while coding, testing, and maintenance phases had higher variability(10-50%). Upgrades from GPT-3.5 to GPT-4 improved reliability.  Conclusion: Despite improvements, ChatGPT still exhibits non-negligible errorrates varying by domain, task, and SDLC phase. Full reliance without humanoversight remains risky, especially in critical settings. Continuous evaluationand critical validation are essential to ensure reliability andtrustworthiness.

Why you shouldn't fully trust ChatGPT: A synthesis of this AI tool's error rates across disciplines and the software engineering lifecycle