Build RAG on AWS Cloud

Build production-ready Retrieval-Augmented Generation (RAG) applications on AWS using Amazon Bedrock, OpenSearch, Lambda, API Gateway, and S3. Gain hands-on experience in building scalable, secure, and enterprise-grade Generative AI solutions with real-world projects and certification.

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Build End-to-End RAG Applications on AWS

Hands-On with Amazon Bedrock & OpenSearch

Deploy Secure Serverless Generative AI Solutions

Enterprise training for teams:

RAG

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Some of the highlights of this course.

60+ Hours Course Duration

20 Practical Labs Hands-On Labs

1 End-to-End RAG Application Capstone Project

7+ Core Services AWS Services Covered

20 Modules Modules Included

Industry-Recognized Completion Certificate Certification

Gain practical experience in building enterprise-grade Retrieval-Augmented Generation (RAG) applications on AWS through 60+ hours of instructor-led training, 20 hands-on labs, real-world projects, and production-ready cloud deployment practices.

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.

what will you get

Key Features & Highlights

End-to-End RAG Implementation

Build complete Retrieval-Augmented Generation pipelines from document ingestion to intelligent response generation using AWS managed services.

Amazon Bedrock Integration

Learn to integrate foundation models and embedding generation using Amazon Bedrock for scalable Generative AI applications.

Vector Search with OpenSearch

Implement vector indexing and semantic retrieval using Amazon OpenSearch Service for enterprise-grade knowledge retrieval.

Serverless API Deployment

Deploy secure and scalable RAG APIs using AWS Lambda and API Gateway for production-ready AI applications.

This course provides practical, hands-on training to design, build, secure, and deploy enterprise-grade Retrieval-Augmented Generation applications on AWS using production-ready cloud architecture and best practices.

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Why do Build RAG on AWS Cloud at Nevo Learn

Experiential Learning

Learn like never before. Not just learning, you interact and gain real experience.

Get practical, real-world learning experience
Engage in interactive sessions and activities
Apply concepts through hands-on exercises

Skill Development

Build skills that matter. Go beyond theory and develop job-ready expertise.

Track and measure your skill progress
Identify strengths and areas for improvement
Gain industry-relevant knowledge and tools

Career Success

Achieve your career goals with structured learning and expert guidance.

Learn from industry experts and mentors
Prepare for certifications and real-world challenges
Boost your career growth with in-demand skills

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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

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About Build RAG on AWS Cloud Certification

Follow these simple steps to earn your professional certification and validate your expertise.

Enroll in the AWS RAG Training program.

Step 1

Complete all 20 training modules and practical labs.

Step 2

Participate in hands-on implementation exercises using AWS services.

Step 3

Build and deploy an end-to-end RAG application as a capstone project.

Step 4

Complete project validation and technical assessment.

Step 5

Enroll in the AWS RAG Training program.

Step 1

Complete all 20 training modules and practical labs.

Step 2

Participate in hands-on implementation exercises using AWS services.

Step 3

Build and deploy an end-to-end RAG application as a capstone project.

Step 4

Complete project validation and technical assessment.

Step 5

Receive the course completion certification after successful evaluation.

🎓 Certification

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Prerequisites

Basic understanding of cloud computing concepts, APIs, and web applications is recommended. Familiarity with AWS services and basic programming knowledge will be helpful for hands-on labs and project implementation. An active Amazon Web Services account is required for practical exercises.

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Who should attend the Build RAG on AWS Cloud training

This course is ideal for professionals and learners who want to build scalable Generative AI applications and deploy Retrieval-Augmented Generation architectures on AWS Cloud. It is suitable for Generative AI Developers, AWS Developers, Cloud Engineers, DevOps Engineers, AI/ML Engineers, Data Engineers, Software Developers, Technical Architects, IT professionals transitioning into AI, and final-year engineering students looking to build practical cloud AI skills.

Generative AI Developer

RAG Application Developer

AWS Cloud Engineer

Cloud AI Engineer

AI/ML Engineer

DevOps Engineer

Data Engineer

AWS Solutions Architect

LLM Application Engineer

AI Integration Specialist

Cloud Automation Engineer

AI Platform Engineer

COMMON QUESTIONS

Build RAG on AWS Cloud FAQs

This course is suitable for developers, cloud engineers, AI/ML engineers, DevOps professionals, and students interested in Generative AI on AWS.

No. Basic cloud knowledge is helpful, but prior AWS experience is not mandatory.

Basic programming knowledge is recommended for hands-on labs.

Yes. The course starts with foundational concepts and gradually moves into advanced RAG implementation.

Yes, an active Amazon Web Services account is required for labs and project deployment.

Yes. Final-year students and entry-level professionals can join and build practical cloud AI skills.

Yes, but technical familiarity with APIs and cloud basics will help.

Basic understanding of cloud computing, APIs, and application workflows.

No, there is no entrance test for enrollment.

Yes, trainer-guided support is provided during practical sessions.

Skills Covered

What Will You Learn?

RAG Architecture Design

Amazon Bedrock Integration

Vector Search Implementation

OpenSearch Configuration

AWS Lambda Deployment

API Gateway Setup

IAM Policy Management

Prompt Engineering

Cloud Monitoring with CloudWatch

Generative AI Cost Optimization

Serverless AI Application Deployment

Enterprise AI Security Best Practices

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Soaring Demand and Accelerated Growth

AWS Generative AI Engineer

Annual Salary

Workers/Salary

₹100K

Entry Level

₹140K

Junior

₹165K

Mid-Level

₹190K

Senior

₹230K

Expert

Hiring Companies

Design, build, and deploy enterprise-grade Retrieval-Augmented Generation (RAG) applications on AWS using managed services like Amazon Bedrock, OpenSearch, Lambda, and S3. Responsibilities include implementing vector search pipelines, integrating LLMs, optimizing cloud AI workloads, managing API deployments, securing AI infrastructure with IAM, and monitoring performance using CloudWatch.

FOR AWS Generative AI Engineer

Annual Salary

Workers/Salary

₹100K

Entry Level

₹140K

Junior

₹165K

Mid-Level

₹190K

Senior

₹230K

Expert

Hiring Companies

Design, build, and deploy enterprise-grade Retrieval-Augmented Generation (RAG) applications on AWS using managed services like Amazon Bedrock, OpenSearch, Lambda, and S3. Responsibilities include implementing vector search pipelines, integrating LLMs, optimizing cloud AI workloads, managing API deployments, securing AI infrastructure with IAM, and monitoring performance using CloudWatch.

for AWS Generative AI Engineer

AWS Generative AI Engineers are responsible for building scalable AI applications powered by cloud-native architecture. They develop production-ready RAG systems, improve knowledge retrieval accuracy, optimize infrastructure costs, and ensure secure deployment of Generative AI applications for enterprise use cases.

Got questions?

Still have a question? Get in Touch with our Experts

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Set your teams up with this course

Group Discount Available

This course equips learners with practical expertise to design, build, and deploy enterprise-grade Retrieval-Augmented Generation (RAG) applications on AWS Cloud. By working with managed services like Amazon Bedrock, OpenSearch, Lambda, and S3, participants gain real-world cloud AI implementation skills, strengthen their Generative AI knowledge, and improve career readiness for high-demand AI and cloud roles.

Top Companies Rely on Us to Upskill Their Workforce

BENEFITS

Transforming your team

Gain hands-on experience building production-ready RAG applications on AWS.

Develop expertise in Amazon Bedrock, vector search, and serverless AI deployment.

Learn enterprise security, monitoring, and cost optimization for Generative AI workloads.

Build a capstone project and earn an industry-recognized certification to enhance career opportunities.

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