Advanced RAG Logo
Cohort 1 Completed · Read the reviews ★★★★★ Participants from Google, Meta, Walmart, Capital One & More

Stop Building RAG Prototypes.
Start Shipping Production RAG.

The only Instructor-led Live 6-week course that takes you from a basic Software professional to an AI Engineer ready for Big Tech Companies.

3 hrs/wk

1.5h theory + 1.5h hands-on coding

Zero Notebooks

Production-ready templates

Live Q&A

Direct instructor access

🚀  Cohort 2 starts July 18th. Secure your spot before it fills up.

The Course Toolkit

Technical Stack Summary

Orchestration

  • LangGraph
  • Python
  • Docker

Database & Storage

  • Qdrant
  • Weaviate

Ingestion & Parse

  • GPT-4o
  • ColPali
  • Unstructured

Retrieve & Rank

  • BM25
  • Cross-Encoders (BGE)
  • Tavily (Search)

Eval & Observe

  • Ragas
  • Arize Phoenix
  • OpenTelemetry
  • NeMo Guardrails

Infrastructure

  • FastAPI
  • Google Cloud Run
  • Gemini Flash

The 6-Week Advanced-RAG Architecture

A unified, visual blueprint of the production-grade RAG pipeline you will build.

Advanced RAG Architecture Diagram showing parsing, chunking, embedding, vector databases, search logic, re-ranking, LLM prompt generation, and evaluation.

What You'll Walk Away With

Concrete skills, not just theory. Every week ends with production-ready code you can use immediately.

Move from RAG POC to production-ready systems in 6 weeks

Implement hybrid search, reranking, and semantic caching

Build evaluation frameworks that catch failures before they reach users

Reduce retrieval latency and LLM costs through production-grade MLOps

Design Agentic RAG patterns that reason over complex, multi-step queries

Walk away with a portfolio of hands-on code ready to deploy

Learn from Practitioners, Not Just Educators

Your instructors have built and shipped RAG systems in production — they teach from hard-won experience, not textbooks.

Ram Seshadri, AI Technical Solutions Architect and course instructor

Ram Seshadri

AI Technical Solutions Architect, Big Tech

Creator of AutoViz · 5.5M+ Downloads

Ram is a senior-level AI Technical Solutions Architect at a Big Tech company and a prominent figure in the Python data science community. With over two decades of hands-on experience, he is best known for creating widely-used open-source Python libraries — including AutoViz (5.5M+ downloads) — that automate complex machine learning tasks. His real-world production expertise grounds every module of this course.

AutoViz CreatorOpen Source20+ Yrs ExperienceBig Tech Architect
Cornellius Yudha Wijaya, Data Scientist and AI Engineer

Cornellius Yudha Wijaya

CPO, ARIF Analytics

5.4K followers · 2.6M+ Views

Cornellius is a Data Scientist, AI Engineer, and prolific technical writer with 6+ years of experience building data-driven SaaS products and Generative AI (LLM) solutions. As Chief Product Officer at ARIF Analytics, he bridges the gap between cutting-edge AI research and production-grade applications — bringing a rare mix of depth and accessibility to every lesson.

Data ScienceGenerative AILLM SystemsTop 1000 Writer

"We built this course because we kept seeing the same painful pattern: brilliant engineers spending months re-discovering lessons the RAG community already learned the hard way. This course is our way of compressing 2+ years of production experience into 6 focused weeks — so you can skip straight to building great systems."

— Ram & Cornellius, Course Instructors

This Course is for you

This is an advanced, practitioner-focused program. It's intentionally not for everyone.

You'll thrive here if you're a...

  • Software Engineer building GenAI features in production
  • Data Scientist moving beyond notebook experiments to deployed systems
  • ML Engineer responsible for LLM infrastructure and retrieval quality
  • Technical Lead evaluating RAG architectures for your organization
  • AI Product Manager who wants to have informed technical conversations

⚠️ This may not be the right fit if you...

  • Have never worked with Python or LLM APIs
  • Are looking for a beginner introduction to AI or machine learning
  • Want a self-paced course with no live interaction or community
  • Are purely a business leader with no hands-on technical role
5/5 stars · 100% recommend rate

What Cohort 1 Participants Say

Real feedback from engineers and technical leaders who completed the program.

The Advanced-RAG course is the ultimate masterclass for bridging the gap between AI prototypes and scalable enterprise products. The curriculum shifts the focus from simple plumbing to strategic LLM refining—tackling the hardest production problems like holistic evaluation metrics, defensive architecture, and optimizing the cost equation through semantic caching and precision reranking. An absolute must-take for anyone looking to build production-ready AI!

D

Fortune 500 Tech Company · Cohort 1

The Advanced RAG course gave me a much clearer understanding of how RAG systems work beyond the basics—especially around architecture choices, retrieval strategies, evaluation, chunking, embeddings, reranking, and practical trade-offs in production settings. The course helped me think more critically about building reliable GenAI applications.

S

Enterprise Software · Cohort 1

Fantastic course with immense practitioner insights. Course had zero fluff and had both breadth and depth across the practical aspects of RAG. The architecture frameworks provided gave me tools to assess key bottlenecks in our production pipeline immediately. I strongly recommend this course to everyone interested in RAG.

R

AI-First Startup · Cohort 1

Excellent teaching on a VERY important topic. What separates this from other RAG content is the production focus—the instructors clearly have first-hand experience shipping systems at scale, not just teaching theory. I recommend this course for everyone using RAGs and Agentic AI.

J

Healthcare Tech · Cohort 1

Strong coverage of practical and advanced RAG concepts with a great balance between theory and real-world application. Helpful discussion of architecture patterns and trade-offs. Content was immediately applicable for engineers building production AI systems—I deployed improvements the week after completing the course.

A

Global Financial Services · Cohort 1

6-Week Production RAG Curriculum

From foundational architecture to advanced Agentic RAG — every week builds directly on the last, with hands-on code you ship.

W1
🏗️

Foundational RAG & Chunking Strategies

  • The Naive RAG Baseline
  • Chunking & Embedding
  • Multimodal and advanced chunking
W2
🔍

Improving-Recall Retrieval: Hybrid Search

  • Dense & Sparse Indexing
  • Implementing Hybrid Search (BM25/SPLADE with vector search)
W3
🎯

Enhancing Precision: Re-ranking

  • Cross-Encoder Optimization
  • Re-ranker implementation
  • Metadata filters
W4
🛡️

Evaluation, Guardrails & Observability

  • Offline & Online evaluation
  • Faithfulness, Context Recall, Answer Relevancy
  • PII/Toxicity Guardrails
  • Observability setup
W5
⚙️

MLOps: Deploying, Performance, Latency & Cost

  • Caching strategies
  • Semantic Caching
  • Managing latency and inference costs
W6
🤖

Complex Reasoning & Agentic RAG

  • Tool Use & Orchestration Patterns
  • Advanced Agentic RAG patterns
  • Self-reflection and planning

3 hrs/week

Live session + exercises

Zero Notebooks

Production-ready templates

Live Q&A

Direct instructor access

💼

Get Reimbursed by Your Employer

Most participants expense this course through their company's L&D budget. Join the waitlist and we'll send you a ready-to-send reimbursement email template for your manager.

Get Reimbursement Template

Enrollment is now Open

Cohort 2 starts July 18th. Join now to secure your spot and start transforming your AI applications.

Enroll Today

Frequently Asked Questions

Everything you need to know before joining the waitlist.

Still have questions?

Email us directly →

Cohort 2 Enrollment Open

Your RAG System Deserves to Be in Production — Not Just a Demo.

Secure your spot in the next cohort starting on July 18th.

Enroll Now — July 18th

Spots are limited to maintain an intimate cohort environment.