Pocket Prompt Engineer (PPE) is an AI-powered writing and prompt-optimization platform designed to help learners, creators, and professionals generate clearer, more effective outputs with structured guidance. As the product architect and co-creator, I built PPE as a multi-language prompt-engineering system capable of transforming rough ideas into high-quality, instruction-ready content through a standardized logic framework.

At the core of PPE is the BAIQL Engine (Behavioral AI Query Logic)—a structured, modular framework I designed to ensure consistent, high-fidelity responses across a wide range of use cases. The engine applies layered contextual logic, metadata tagging, and role-based transformation rules to convert user intent into optimized AI queries. This creates predictable output quality even for users with limited prompting experience.

I led the development of PPE’s full template architecture, covering business writing, educational content, marketing, technical documentation, and creative ideation. The platform supports multi-step workflows, multilingual prompt logic, and domain-specific output structures tailored for clarity, accuracy, and ease of execution.

In addition to technical development, I guided PPE’s SBIR/NSF grant commercialization roadmap—defining the product’s technical scope, user pathways, validation plan, and early-stage market positioning. This included building investor-ready narratives, ROI modeling, competitive analysis, and a multi-year research strategy focused on learning science, AI-assisted writing, and adaptive content generation.

PPE demonstrates my ability to design AI-driven writing systems, apply structured prompt-engineering methods, and build scalable frameworks that improve user outcomes across educational and professional settings.