D24
Advanced SpecializationData & AIBeginner4 weeks
RAG (Retrieval-Augmented Generation)
RAG (Retrieval-Augmented Generation) is a free 4-week beginner course explaining how document retrieval and embeddings ground AI answers in real data instead of guesses, reducing hallucination compared to a plain chatbot. Learners build a working RAG pipeline covering chunking, vector and hybrid search, reranking, and evaluating whether retrieved answers are actually correct.
Learn how RAG grounds AI answers in your own documents and data, reducing hallucination.
What you'll learn
How document retrieval and embeddings work
Building a simple RAG pipeline end to end
Why RAG reduces hallucination versus a plain chatbot
Evaluating whether retrieved answers are actually correct
Course details
Duration
4 weeks
Best for
Anyone who wants AI answers grounded in real documents, not guesses
This course is free
Course Curriculum
A module-by-module breakdown of what you'll cover in RAG (Retrieval-Augmented Generation).
01Basic
6 topicsWeek 1
- What is RAG, and why do LLMs need it?
- Embeddings and vector search, explained simply
- Chunking: splitting documents for retrieval
- Vector databases: choosing and configuring embedding storage
- RAG architectures: naive, advanced, and modular pipelines
- Project: Build a minimal RAG pipeline over your own notes
02Intermediate
7 topicsWeek 2
- Reranking and improving retrieval quality
- Hybrid search: combining keyword and vector retrieval
- Query transformation, expansion, and contextual compression
- Chunking strategies in depth: fixed, semantic, and recursive trade-offs
- Prompt construction: getting the LLM to use context correctly
- Handling ambiguous, out-of-scope, and unanswerable queries
- Project: Add hybrid search and abstention to your RAG pipeline
03Advanced
5 topicsWeek 3
- Validating data and preserving document identity
- Measuring retrieval quality: precision@k, recall@k, MRR, and nDCG
- Binding claims to evidence and handling multi-hop questions
- Agentic RAG: retrieval as a tool call, not a fixed step
- Evaluation, ownership, and observability for RAG systems
04Pro
6 topicsWeek 3
- Keeping a knowledge base fresh: reindexing and incremental updates
- GraphRAG and multi-modal retrieval over images and tables
- Cost, latency, and multi-tenant considerations
- Monitoring retrieval quality over time
- Production RAG failure modes: a diagnostic field guide
- Project: Design a production reindexing and monitoring plan
05AI Pro
3 topicsWeek 4
- Using Claude, ChatGPT, and Perplexity to design and debug your RAG pipeline
- Verifying AI-suggested RAG fixes before trusting them
- Project: Use AI to diagnose and fix a real retrieval failure, with measurement
Tools & Technologies
Hands-on practice with the same tools used by working professionals.
VecVector Search
LLMLLM APIs
ChaChatGPT
ClaClaude
PerPerplexity
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Communication & Soft Skills
Built into every course, alongside the technical curriculum.
Communication Skills
Practice explaining technical work clearly to teammates, managers, and clients.
Practical AI Skills
Hands-on training in using AI tools well — prompting, everyday workflows, and where AI actually helps versus where it doesn't.
Teamwork & Collaboration
Work in small project teams using real workflows like stand-ups and code reviews.
Problem Solving
Build the habit of breaking down ambiguous problems into clear, solvable steps.
Time Management
Learn to plan, prioritize, and deliver project work against realistic deadlines.
Presentation Skills
Present your project work and results with confidence, as you would to a client.
Resume & Profile Building
Build a strong resume, LinkedIn profile, and project portfolio that recruiters notice.
Interview Preparation
Mock interviews and portfolio reviews to get you ready for real job applications.
Career & Placement Support
100% of students enrolled in RAG (Retrieval-Augmented Generation) get full access to placement support — real tools and guidance, not just a promise. This is support and access, not a guaranteed job outcome.
Resume Builder
A resume auto-built from your real completed projects and certificates, ready to export.
Shareable Portfolio
A public portfolio page showcasing your approved projects, with a link you can share with employers.
Interview Preparation
Guidance on presenting your projects and experience clearly in real interviews.
Peer & Mentor Community
Ongoing access to our learner community for questions, feedback, and support after you finish.
What Your Certificates Look Like
Real, verifiable certificates with your name, RAG (Retrieval-Augmented Generation), and a QR code anyone can scan to confirm they're genuine — one for completing the course, and a separate one if you take the internship track.
Course Completion Certificate
SAMPLE
Data24ZoneCertificate of Completion · Free Course
Illustrative recipient
Your Name Here
Example certificate for completion of
RAG (Retrieval-Augmented Generation)
- Certificate No.
- D24-SAMPLE
- Mode
- Self-Paced
- Duration
- 4 weeks
- Example issue date
- On completion
Sample only — not a real, issued certificate.
Verification is available only on issued certificates.
Internship Completion Certificate
SAMPLE
Data24ZoneCertificate of Internship
Illustrative recipient
Your Name Here
Example certificate for completion of
RAG (Retrieval-Augmented Generation)
- Certificate No.
- D24-SAMPLE
- Mode
- Internship
- Duration
- 4 weeks
- Example issue date
- On completion
Sample only — not a real, issued certificate.
Verification is available only on issued certificates.
Frequently Asked Questions
What will I learn in the RAG (Retrieval-Augmented Generation) course?
You'll learn: How document retrieval and embeddings work; Building a simple RAG pipeline end to end; Why RAG reduces hallucination versus a plain chatbot; Evaluating whether retrieved answers are actually correct.
What tools and technologies does the RAG (Retrieval-Augmented Generation) course cover?
This course covers: Vector Search, LLM APIs, ChatGPT, Claude, Perplexity.
Is the RAG (Retrieval-Augmented Generation) course beginner-friendly?
Yes — this is a beginner-level course.
How long does the RAG (Retrieval-Augmented Generation) course take to complete?
The course is designed to be completed in approximately 4 weeks, depending on your pace.
What enrollment options are available for RAG (Retrieval-Augmented Generation)?
Internship (₹2,499) — Work through real projects with our team and earn an internship certificate (course certificate not included). Self-Paced (₹1,999) — Learn anytime with the full recorded curriculum and community access.
Is there an internship option for RAG (Retrieval-Augmented Generation)?
Yes — the Internship track means working through real projects with our team and earning a separate Internship Certificate.
Will I get a certificate after completing RAG (Retrieval-Augmented Generation)?
Yes. Self-paced enrollment earns a course completion certificate once your final project is reviewed and approved. Internship enrollment earns an internship certificate once your internship projects are approved. Every certificate is publicly verifiable on our website and can be added directly to your LinkedIn profile.
Is placement support included with RAG (Retrieval-Augmented Generation)?
Yes — every enrolled student gets full access to our placement support: resume building, a shareable project portfolio, mock interview preparation, and guidance applying what you've built to real job applications. This is support and access, not a guaranteed job outcome — how quickly it leads to an offer depends on the market and your own effort.
