Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

AWS GenAI: RAG, AI Agents and Responsible AI

KodeKloud via Coursera

Go to class Write review

Overview

Google, IBM & Meta Certificates – 40% Off

One plan covers every Professional Certificate on Coursera.

Unlock All Certificates

Deploying generative AI applications reliably, ethically, and at enterprise scale is a significant challenge. This course provides the advanced AWS architecture skills and responsible AI governance frameworks needed to implement trusted systems. You will master the production toolkit for foundation models on AWS, including model selection, Amazon Bedrock configurations, Retrieval-Augmented Generation (RAG) with vector databases, multi-step AI agents, prompt engineering, fine-tuning decision frameworks, and LLM benchmarking. A comprehensive responsible AI module addresses bias mitigation, dataset curation, explainability, legal risk management, and human-centered design through workplace role-play scenarios using AWS tools. The course concludes with a Python capstone project: building a production-ready Multimodal AI Knowledge Assistant that integrates text/image generation, function calling, a RAG pipeline, structured outputs, content moderation, and a documented governance layer. This course is designed for ML engineers, cloud architects, data scientists, and AI governance professionals with OpenAI API experience ready to deploy and govern enterprise AI systems on AWS.

Syllabus

RAG, Vector Databases, Agents, Prompt Engineering

This module explores how to design, customize, and optimize foundation model applications on AWS. You'll learn how to select pre-trained models, adjust inference parameters, and leverage retrieval-augmented generation (RAG) with vector databases. Additionally, the module covers multi-step task agents, prompt engineering techniques, fine-tuning processes, and performance evaluation to ensure efficient deployment of foundation models.

Bias Mitigation, Explainability, Human-Centered Design

This module focuses on the ethical and practical considerations for developing responsible AI applications. You’ll explore key features of responsible AI, tools for identifying responsible practices, and strategies for mitigating bias in datasets. Additionally, the module covers legal risks in generative AI, transparent and explainable models, and human-centered design principles to ensure ethical and effective AI deployment.

Capstone Project: Multimodal AI Knowledge Assistant

In this capstone project, learners build a production-ready multimodal AI knowledge assistant in Python that integrates the full skill set developed across the specialization. The application combines OpenAI Chat Completions with advanced prompt engineering, DALL-E image generation via function calling, a Retrieval-Augmented Generation (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.