Course Description

QUICK FACTS
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
Climb the ladder of success with structured role progression.

The most effective project-based immersive learning experience The most effective project-based immersive learning experience The most effective project-based immersive learning experience

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