The capstone is a production-ready Multimodal AI Knowledge Assistant built in Python. It integrates OpenAI Chat Completions with advanced prompt engineering, DALL-E image generation triggered via function calling, a RAG pipeline backed by a vector database, structured outputs for consistent responses, content moderation, and a documented responsible AI governance layer. This single deliverable demonstrates end-to-end AI engineering proficiency — from first API call to enterprise-ready architecture with ethical safeguards — and is designed to be portfolio-ready.

The dedicated module on responsible AI covers the six core features of responsible AI (explainability, fairness, bias, robustness, privacy, and governance), tools for identifying responsible practices, responsible model selection, legal risks in generative AI, dataset characteristics and bias mitigation, data curation for balanced datasets, and human-centered design principles using AWS tools. You'll apply each topic through role play scenarios — including a bias mitigation review and a legal risk navigation scenario — and the capstone project requires you to document and implement a responsible AI governance layer as part of your final deliverable.

RAG is an architecture pattern that grounds an AI model's responses in a specific knowledge base by retrieving relevant content from a vector database before generating a response — solving the limitation of LLMs that can't access current or domain-specific information. In this course, you'll implement a complete RAG pipeline on AWS using Amazon Bedrock and vector databases. In the capstone project, you'll integrate this pipeline into a full Multimodal AI Knowledge Assistant, where it grounds AI responses in a domain-specific knowledge base you define.

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When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.