D24
Data & AIIntermediate10 weeks
RAG AI & Automation
Learn to build retrieval-augmented AI systems that ground large language models in real data, then automate business workflows around them end to end.
What you'll learn
Building retrieval pipelines with embeddings and vector databases
Advanced retrieval techniques: hybrid search, re-ranking, and query rewriting
Automating multi-step workflows that combine LLM calls with business systems
Evaluating and deploying RAG systems with production-grade monitoring
Course details
Duration
10 weeks
Best for
Developers comfortable with Python who want to build production-grade AI search and automation systems
On completion
Verified certificate & placement assistance
Compare enrollment modes
| Mode | Price | What you get |
|---|---|---|
| Live Class | ₹4,999 | Instructor-led live cohort with doubt-clearing sessions — includes both the course and internship certificates. |
| Recorded Batch | ₹2,999 | Full recordings of a completed live batch — includes both the course and internship certificates. |
| Internship | ₹2,499 | Work on 4 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. |
Choose how you learn
Course Curriculum
A module-by-module breakdown of what you'll cover in RAG AI & Automation.
01
Getting Started & Environment Setup
Week 1- Getting API access to Claude or OpenAI
- Installing LangChain and LlamaIndex
- Creating a free Pinecone account (or setting up pgvector)
- Installing n8n via Docker and setting up Langfuse/LangSmith
02
LLM & API Foundations
Weeks 2–3- Calling LLM APIs with structured JSON output
- Managing tokens, context windows, and rate limits
- Environment and secrets management
- Python fundamentals for AI engineering
03
Embeddings & Vector Search
Week 4- Embedding models and similarity metrics
- Working with Pinecone, Chroma, FAISS, and pgvector
- Index types: HNSW and IVF
- Benchmarking retrieval latency and accuracy
04
Document Processing & Chunking
Weeks 5–6- Parsing PDFs, HTML, and scanned documents
- Fixed vs. semantic chunking strategies
- Metadata tagging and overlap tuning
- Table and image extraction
05
Prompt Engineering for Grounded Answers
Week 7- System prompts for grounded, citation-backed answers
- Few-shot prompting and hallucination guardrails
- Context window budgeting
- Multi-turn retrieval conversations
06
Advanced Retrieval & Automation
Weeks 8–9- Hybrid search combining BM25 and vector retrieval
- Cross-encoder re-ranking and query rewriting
- Building automation workflows with n8n and LangChain/LangGraph
- Tool-calling orchestration and error handling
07
Evaluation & Production Deployment
Week 10- Scoring faithfulness, relevance, and precision with RAGAS
- Hallucination detection techniques
- Deploying with FastAPI and Docker
- Tracing, cost, and latency monitoring
Tools & Technologies
Hands-on practice with the same tools used by working professionals.
ClaClaude / OpenAI API
PinPinecone / pgvector
LanLangChain
LlaLlamaIndex
n8nn8n
CroCross-encoder rerankers
RAGRAGAS
LanLangfuse / LangSmith
Real-Time, Industry-Based Projects
This is project-based learning modeled on real industry scenarios — you'll build portfolio-ready work as you go, not just watch lectures.
Internal knowledge-base assistant — a retrieval-augmented Q&A tool over company docs with source citations
Hybrid search & re-ranking benchmark — compare vector-only search against a BM25 plus re-ranking pipeline
Support-ticket triage automation — auto-classify tickets, retrieve relevant content, and draft replies for human review
Low-hallucination compliance RAG — a citation-strict retrieval system with an explicit 'I don't know' fallback
RAG evaluation harness — a repeatable test suite scoring faithfulness and relevance across model changes
Every course includes a guided internship on top of your project work — so you graduate with real experience, not just a certificate.
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.
Who's teaching this
Prasanth J
Founder · Zoho One Expert & Corporate Trainer
Microsoft Certified: Power BI Data Analyst AssociateBefore Data24Zone trained anyone else, its founder was already doing the work — delivering corporate and campus training in data analytics, Power BI, and the Zoho suite across colleges and companies. 10,000+ learners trained, same standard every course is built to.
Meet the founderCareer & Placement Support
100% of students enrolled in RAG AI & Automation 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.
Student Reviews
“I'd only used LLM APIs for basic chat before this. Building a hybrid search pipeline and actually measuring the precision gains changed how I think about retrieval.”
Aditya Rao
Backend Developer
“The evaluation module was the highlight for me — learning to score groundedness with RAGAS instead of eyeballing answers made my work feel production-ready.”
Sneha Kulkarni
Data Scientist
Frequently Asked Questions
What will I learn in the RAG AI & Automation course?
You'll learn: Building retrieval pipelines with embeddings and vector databases; Advanced retrieval techniques: hybrid search, re-ranking, and query rewriting; Automating multi-step workflows that combine LLM calls with business systems; Evaluating and deploying RAG systems with production-grade monitoring.
What tools and technologies does the RAG AI & Automation course cover?
This course covers: Claude / OpenAI API, Pinecone / pgvector, LangChain, LlamaIndex, n8n, Cross-encoder rerankers, RAGAS, Langfuse / LangSmith.
Is the RAG AI & Automation course beginner-friendly?
This is a intermediate-level course. It still starts with a "Getting Started & Environment Setup" module covering tool installation, but assumes some prior familiarity with the subject.
How long does the RAG AI & Automation course take to complete?
The course is designed to be completed in approximately 10 weeks, depending on your pace.
What enrollment options are available for RAG AI & Automation?
Live Class (₹4,999) — Instructor-led live cohort with doubt-clearing sessions — includes both the course and internship certificates. Recorded Batch (₹2,999) — Full recordings of a completed live batch — includes both the course and internship certificates. Internship (₹2,499) — Work on 4 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 AI & Automation?
Yes — enrolling in the Internship track (or the Live/Recorded tracks, which include it) means working through 4 real projects with our team and earning a separate Internship Certificate, in addition to the course completion certificate for Live/Recorded enrollments.
Will I get a certificate after completing RAG AI & Automation?
Yes. Self-paced enrollment earns a course completion certificate once your final project is reviewed and approved. Internship enrollment earns an internship certificate once all 4 internship projects are approved. Live and Recorded enrollments earn both. Every certificate is publicly verifiable on our website and can be added directly to your LinkedIn profile.
What kind of projects will I build in RAG AI & Automation?
You'll work on real projects such as: Internal knowledge-base assistant — a retrieval-augmented Q&A tool over company docs with source citations; Hybrid search & re-ranking benchmark — compare vector-only search against a BM25 plus re-ranking pipeline; Support-ticket triage automation — auto-classify tickets, retrieve relevant content, and draft replies for human review; Low-hallucination compliance RAG — a citation-strict retrieval system with an explicit 'I don't know' fallback; RAG evaluation harness — a repeatable test suite scoring faithfulness and relevance across model changes.
Is placement support included with RAG AI & Automation?
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.
