Holiday Special | Enjoy 20% OFF – Celebrate the Season with Big Savings!Holiday Special | 20% OFF!
Build RAG on AWS Cloud Using AWS Managed Services for Generative AI Developers
Master Retrieval-Augmented Generation (RAG) on AWS using managed services like Amazon Bedrock, OpenSearch, Lambda, API Gateway, and S3. Build secure, scalable GenAI applications that retrieve enterprise data, reduce hallucinations, and deploy production-ready AI solutions on AWS Cloud.
Build RAG on AWS Cloud Course Overview
Key Features
Who All Can Attend This Build RAG on AWS Cloud Course?
This program is designed for professionals and learners aiming to build scalable Generative AI applications on AWS Cloud using industry-standard architecture and best practices.Prerequisites To Take Build RAG on AWS Cloud Using AWS Managed Services for Generative AI Developers
Basic understanding of cloud computing concepts and familiarity with APIs or web applications is recommended. Prior exposure to AWS services is beneficial but not mandatory. Basic programming knowledge will help during hands-on labs. An active AWS account is required for practice exercises and pr
- Upskill or reskill your teams
- Immersive Learning Experiences
- Private cohorts available
- Advanced Learner Analytics
- Skills assessment & benchmarking
- Platform integration capabilities
- Dedicated Success Managers
- Upskill or reskill your teams
- Immersive Learning Experiences
- Private cohorts available
- Advanced Learner Analytics
Play Intro Video
Next Cohort starts in 2 days
This AWS RAG training empowers professionals to design and deploy enterprise-grade Generative AI systems in secure cloud environments. Learners gain practical expertise in configuring AWS managed services to build scalable RAG architectures that meet real-world production requirements.
High Demand for Build RAG on AWS Cloud Using AWS Managed Services for Generative AI Developers
Soaring Demand and Accelerated Growth
Average Salary
Skills Focused
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
Concepts
What embeddings are (vector meaning in simple language)
Similarity search and why it powers retrieval
Embeddings vs keywords: when each helps
Lab 6
Generate embeddings for a small chunk dataset (guided)
Inspect example vectors (no math deep dive)
Store embeddings output for loading into vector store
Concepts
What is Amazon Bedrock and why it’s useful
Choosing an LLM for Q&A tasks (beginner guidance)
Token basics and cost awareness
Lab 7
Enable Bedrock access (guided)
Test a simple prompt for Q&A
Test embeddings generation through Bedrock-supported approach
Concepts
What is a vector database (in plain English)
OpenSearch basics: index, documents, search
Why OpenSearch is used in enterprise AWS setups
Lab 8
Create/Open an OpenSearch domain (guided)
Create an index for vectors + metadata
Load sample vectors into OpenSearch
Concepts
Query → embedding → similarity search → top-k results
What is “top-k” and “score threshold”
Common retrieval mistakes and how to fix them
Lab 9
Run a vector similarity search in OpenSearch
Retrieve top chunks for a question
Verify results relevance manually
Concepts
Why prompt structure matters in RAG
Prompt template: system + user + retrieved context
Guardrails: “answer only from context” pattern
Lab 10
Build a basic prompt template
Inject retrieved chunks into prompt
Generate an answer using Bedrock LLM and verify citations
Concepts
Putting it all together: ingest → embed → store → retrieve → answer
Data flow and dependencies between components
Lab 11
Execute a full end-to-end run with your dataset
Ask 10 test questions and record accuracy
Improve chunking or retrieval settings based on results
Concepts
What is serverless and why Lambda is ideal for RAG APIs
Event-driven basics
Timeout and memory sizing (practical rules)
Lab 12
Create a Lambda function (guided template)
Connect Lambda to OpenSearch + Bedrock
Test with sample payload
Concepts
What is an API and why we need it
REST endpoints for /ask or /chat
Authentication basics (API keys / IAM auth overview)
Lab 13
Create an API Gateway endpoint for the RAG function
Connect API Gateway → Lambda
Test using Postman / curl (trainer guided)
Concepts
Basic web UI flow: input question → API → response
Keeping UI simple for demo and interviews
Lab 14
Use a lightweight sample UI (provided)
Connect UI to API endpoint
Ask questions and display answers + sources
Concepts
Least privilege and why it matters in cloud AI
Securing S3, Lambda, OpenSearch
Handling secrets safely (basic overview)
Lab 15
Apply IAM roles for Lambda with minimal permissions
Lock down S3 bucket policies
Confirm access works and unauthorized access is blocked
Concepts
Why monitoring matters: troubleshooting + cost
Logs vs metrics
Common
RAG issues: slow retrieval, token spikes, timeouts
Lab 16
Enable CloudWatch logs for Lambda and API
Create simple alarms/metrics (guided)
Debug one intentional failure scenario
Concepts
Better chunking and metadata usage
Re-ranking concept (basic idea)
Handling “no answer found” safely
Lab 17
Tune top-k and thresholds
Add “no relevant context” fallback response
Compare before/after answer quality
Concepts
What drives cost: tokens, requests, storage, OpenSearch sizing
Simple cost-saving rules for freshers
Budget alerts basics
Lab 18
Create a basic cost monitoring setup (guided)
Reduce token usage using shorter prompts + context trimming
Estimate monthly cost for a small prototype
Concepts
Project planning: dataset, features, endpoints, testing
What recruiters expect in a RAG demo
Lab 19
Build your own RAG app with your selected documents
Implement ingestion → retrieval → answer → API
Prepare a project README template
Concepts
How to explain RAG architecture in interviews
Common interview questions on RAG + AWS managed services
Portfolio presentation tips
Lab 20
Deliver your capstone demo (2–3 min walkthrough)
Create an architecture diagram (guided)
Build a “talk track” explaining choices and trade-offs
Career Path
Certification Process
Connect With Reps
Frequently Asked Questions
Public batches are conducted online instructor-led. Corporate training can be delivered online or onsite based on requirement.
Basic programming knowledge is helpful, but advanced coding is not required.
Amazon Bedrock, S3, OpenSearch, Lambda, API Gateway, IAM, and CloudWatch.
Yes, the course includes a complete end-to-end capstone RAG application.
Yes, including embeddings generation and LLM-based answer generation.
Yes, managed vector search implementation using Amazon OpenSearch.
RAG retrieves relevant enterprise data before generating responses, improving factual accuracy.
Yes, an AWS account is required for labs and project deployment.
Yes, the curriculum progresses from foundational concepts to advanced implementation.
Generative AI Developer, RAG Developer, Cloud AI Engineer, AWS AI Architect, and related AI cloud roles.
Yes, including token management, infrastructure sizing, and monitoring techniques.
Yes, including IAM policies, least privilege access, and secure API configuration.
The certification validates practical RAG implementation skills aligned with AWS managed services architecture standards.
Banking, Healthcare, SaaS, E-commerce, Enterprise IT, Customer Support, and Knowledge Management systems.
Yes, it strengthens practical knowledge relevant to AWS AI and cloud architecture pathways.
Build RAG on AWS Cloud Using AWS Managed Services is a specialized cloud-focused Generative AI training program designed for developers who want to deploy Retrieval-Augmented Generation (RAG) systems in production environments using AWS infrastructure. As enterprises rapidly adopt Generative AI, organizations increasingly seek professionals who can implement secure, scalable, and cost-optimized RAG architectures on cloud platforms like AWS.
Build RAG on AWS Cloud Using AWS Managed Services for Generative AI Developers is a hands-on, implementation-focused training program designed to help professionals build production-ready Retrieval-Augmented Generation (RAG) applications using AWS native services.
