Continuous Delivery Pipeline for Generative AI Apps in Hospitality

Added Jun 2025 3 design docs

Hospitality companies are adopting generative AI for guest messaging, review responses, and concierge features, but their engineering reality is unforgiving: a broken deployment during a holiday weekend directly costs bookings. AI applications add new failure modes that traditional release processes do not catch, because a code change can pass unit tests while silently degrading model output. Teams need delivery pipelines built with those risks in mind. The intern builds a complete CI/CD pipeline for a generative AI application serving the hospitality sector. The application itself is a Python FastAPI service using LangChain to power guest-facing question answering, and the pipeline around it automates the path to production on AWS: source changes trigger automated builds, a test suite that covers both conventional code behavior and AI output checks, and staged deployment to AWS infrastructure with rollback if checks fail. The intern implements the pipeline configuration, environment management for API keys and model settings, and the deployment scripts that make releases repeatable rather than artisanal. The project teaches DevOps fundamentals in their modern AI-flavored form: pipeline design, automated testing strategies for LLM applications, AWS deployment practices, and the operational mindset that separates hobby projects from services businesses rely on.

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