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Learning Objectives

Course Description

Build RAG on AWS Cloud Overview

Build RAG on AWS Cloud Using AWS Managed Services for Generative AI Developers is a hands-on training program designed to help professionals build production-ready Retrieval-Augmented Generation (RAG) applications using AWS-native managed services. This course covers the complete RAG lifecycle including document ingestion, embedding generation, vector storage, retrieval orchestration, prompt engineering, API deployment, security, monitoring, and cost optimization. Participants will work with Amazon Web Services services such as Amazon Bedrock, Amazon OpenSearch Service, AWS Lambda, Amazon S3, API Gateway, IAM, and CloudWatch to build scalable, secure, and enterprise-grade AI applications. By the end of the program, learners will be able to architect and deploy complete RAG solutions aligned with modern cloud AI standards.

QUICK FACTS

Build RAG on AWS Cloud Curriculum

Concepts

     What is Generative AI vs RAG (simple explanation)

     Why RAG is used in real jobs (support bots, search assistants, knowledge chat)

     RAG flow: Documents → Embeddings → Vector DB → Retrieve → LLM Answer

     “Hallucination” and how retrieval reduces it

Lab 1

     Explore a simple prebuilt RAG demo (provided)

     Identify each RAG component in the demo (documents, retrieval, response)

     Write a basic “RAG checklist” for your own project

Concepts

     AWS Regions, Availability Zones, basic pricing idea

     IAM basics: users, roles, policies (beginner friendly)

     Why managed services matter for freshers

Lab 2

     Create AWS account setup checklist (trainer-guided)

     Create an IAM user/role with minimum permissions (guided policy)

     Enable CloudWatch logging basics

Concepts

     What is object storage, buckets, folders

     Naming, versioning, encryption basics

     How S3 fits into a RAG document pipeline

Lab 3

     Create an S3 bucket for a “knowledge base”

     Upload PDFs/text files

     Enable versioning + basic encryption

     Set correct access permissions (avoid public access)

Concepts

     Why clean text matters for search accuracy

     Common document issues: headers, page numbers, repeated text

     Simple preprocessing steps (no heavy coding)

Lab 4

     Use a guided extraction approach (trainer-provided script / managed flow)

     Convert sample files into clean text format

      ●     Store cleaned output back in S3 (separate folder structure)

Concepts

     What is chunking and why it matters

     Chunk size, overlap (simple rules)

     Metadata: file name, section, page number

Lab 5

     Apply chunking to extracted text using a guided workflow

     Generate chunk files + metadata JSON

      ●     Save chunks to S3 for embedding step

CAREER GROWTH

Your Career Path

Climb the ladder of success with structured role progression.

1

Generative AI Developer

Step 1
2

RAG Application Developer

Step 2
3

Cloud AI Engineer

Step 3
4

AWS AI Solutions Architect

Step 4
5

Senior AI Platform Engineer

Step 5

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Learning Objectives | NevoLearn