I authored a comprehensive “Definition of Good” that became the single source of truth for what a successful TravelAd tagline must be. The document codified quality criteria (clarity, factual grounding, brevity), brand and tone guidelines for both Inform and Inspire variants, rules for appropriateness and policy constraints, and an edge‑case catalog describing how to handle contradictions, slang, and ambiguous reviews. That strategy guided every prompt, informed the evaluation rules, and provided clear pass/fail conditions so outputs could be assessed consistently at scale.

I implemented the end‑to‑end prompting solution myself, doing prompt engineering and integrations across IntelliJ, RStudio, and Swagger. I developed and iterated prompt templates, control parameters, and example-driven guidance to coax dual‑tone taglines from the model, and used IntelliJ for development, RStudio for exploratory data analysis and batching logic, and Swagger to wire generation into the downstream API surface. I versioned prompts and maintained a reproducible development loop so I could test changes, roll back problematic templates, and scale generation across 100k+ properties.

To ensure the outputs met the Definition of Good, I designed and ran an analysis framework that compared generated taglines against standards for tone, appropriateness, and factual content. I combined automated checks with spot human review to surface hallucinations, tone drift, and formatting issues, logged failure modes, and iteratively refined prompts and rules based on those findings. The validation loop made failures visible early, guided targeted fixes, and established a repeatable process for improving quality as the pipeline ran at scale.