AI Prompt Engineering Toolkit

The AI Prompt Engineering Toolkit began as a platform for prompt engineering best practices, but evolved into something more ambitious: a system for predicting prompt failures before they happen. The foundation comes from 129 code reviews I performed at Outlier, where I consistently identified 7 recurring issues in AI-generated applications. These patterns became 7 custom subagents, each with a real name and personality: Amber Williams (responsive design), Kristy Rodriguez (functionality completeness), Micaela Santos (design consistency), Lindsay Stewart (accessibility), Eesha Desai (state management), Daniella Anderson (code quality), and Cassandra Hayes (cross-feature integration). What makes this approach unique is the shift from prompt engineering to context engineering. Rather than overloading an initial prompt with every possible requirement, the subagents deliver domain-specific context just-in-time. A lazy prompt becomes a sophisticated application because my context engineers fill in the missing requirements at the right moment in the right order. The 777-1 experiment is the proving ground: 7 projects built with a general-purpose agent, then refined by all 7 subagents running in separate sessions to avoid attention dilution. Each project produces a case study documenting what issues were found, what patterns emerge, and how the subagent specifications can be improved. The goal is an algorithm with a scoring mechanism that looks at your prompt and predicts multiple areas where the model might fail, references case studies that demonstrate these failures, and provides testing suggestions and prompt improvement strategies.