RAG Masterclass: Build Production Retrieval-Augmented Generation Systems
Master RAG with LangChain, LlamaIndex, Pinecone, Qdrant, and RAGAS. 10 modules, 6 projects, 55+ hours. Learn chunking, embeddings, hybrid search, multimodal RAG, agentic RAG, and production deployment.
Course Snapshot
| Course Code | AIM-602 |
| Duration | 55+ hours |
| Modules | 10 |
| Hands-on Projects | 6 |
| Skill Level | Intermediate to Advanced |
| Frameworks Covered | LangChain, LlamaIndex, Haystack |
| Vector DBs Covered | Pinecone, Qdrant, Weaviate, pgvector |
| Evaluation Tool | RAGAS |
| Certificate | Yes — RAG Masterclass |
| Price | ₹5,999 (Early Bird ₹3,999) |
| Last Updated | July 2026 |
Course Overview
Retrieval-augmented generation, or RAG, is the architecture that makes large language models useful for real business data. Without RAG, an LLM is a brilliant generalist that knows nothing about your documents, your codebase, or your customers. With RAG, that same model becomes a precise analyst that can answer questions grounded in your proprietary data, cite its sources, and refuse to hallucinate. This masterclass takes you from the fundamentals of why RAG works through the full production stack: document processing, chunking strategies, embedding models, vector databases, hybrid search, reranking, and generation. You will build RAG systems with both LangChain and LlamaIndex, understand the trade-offs between them, and learn when to use each. We go deep into advanced techniques like query expansion, HyDE, multi-query retrieval, contextual compression, and graph RAG. You will master RAGAS for evaluation, build multimodal RAG systems that handle images and tables, and implement agentic RAG where the model decides when and what to retrieve. The course culminates in production deployment: serving RAG at scale with caching, streaming, observability, and cost optimization. By the end, you will have shipped six production-grade RAG projects and possess the systematic methodology that distinguishes a toy demo from a system that handles millions of queries reliably.
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Enroll Now — Limited Seats →Who This Course Is Built For
This course is designed for specific professional profiles. If you match any of these, you will get maximum value.
Software Engineers Building AI Applications
You are building chatbots, search systems, or knowledge tools that need to answer questions from your company's documents. You need RAG that works in production, not just in demos.
Data Engineers & ML Engineers
You manage data pipelines and want to build retrieval systems that connect your data warehouse to LLMs. You need to understand embeddings, vector indexes, and search optimization.
AI Architects & Technical Leaders
You are designing the AI architecture for your organization and need to evaluate RAG approaches, choose frameworks, and plan production deployment with proper evaluation and monitoring.
Developers Who Completed Prompt Engineering Pro
You know how to prompt models and now want to ground them in real data. RAG is the natural next step from prompt engineering to production AI systems.
This Course Is NOT For You If:
- Complete beginners who have never used an LLM API — complete Prompt Engineering Pro first.
- Those wanting pure theory without building systems — every module includes hands-on implementation.
- Researchers focused on novel retrieval algorithms — this is an applied engineering course using existing tools.
What You Will Learn
After completing this course, you will be able to:
- 1 You will be able to explain the complete RAG pipeline from document ingestion to generated answer and diagnose failures at each stage.
- 2 You will be able to implement chunking strategies — fixed-size, semantic, sentence-based, and recursive — and choose the right one for your document types.
- 3 You will be able to select embedding models, generate embeddings at scale, and optimize vector storage with quantization and approximate nearest neighbor indexes.
- 4 You will be able to build RAG pipelines with both LangChain and LlamaIndex, understanding the strengths and trade-offs of each framework.
- 5 You will be able to implement advanced retrieval techniques including hybrid search, multi-query retrieval, HyDE, contextual compression, and reranking.
- 6 You will be able to evaluate RAG systems with RAGAS, measuring faithfulness, answer relevance, context precision, and context recall.
- 7 You will be able to build multimodal RAG systems that retrieve and reason over images, tables, and mixed-format documents.
- 8 You will be able to implement agentic RAG where the LLM decides when to retrieve, what to retrieve, and when to stop retrieving.
- 9 You will be able to deploy RAG systems to production with caching, streaming, observability, and cost optimization.
- 10 You will be able to debug and improve underperforming RAG systems using systematic diagnosis and targeted optimization.
Download Free Course Syllabus
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Download Free Syllabus →Complete Course Curriculum
10 modules with detailed lessons. Every lesson includes specific learning points.
MODULE 1 — RAG Fundamentals
4 hours- The hallucination problem: why LLMs make things up and how grounding in retrieved context fixes it.
- RAG vs fine-tuning vs long-context: when each approach is appropriate and their cost trade-offs.
- The RAG architecture at a glance: ingestion, retrieval, and generation stages.
- Document loading: PDFs, web pages, databases, APIs, and the challenges of each format.
- The retrieval stage: embedding queries, searching vector stores, and ranking results.
- The generation stage: assembling context, crafting the prompt, and generating grounded answers.
- Common failure modes: poor chunking, bad embeddings, irrelevant retrieval, and context window overflow.
- The retrieval-generation gap: when the right documents are retrieved but the model still hallucinates.
- Diagnosing failures: tracing the pipeline to identify which stage is the bottleneck.
- A complete RAG pipeline from scratch using only the OpenAI SDK and a simple vector store.
- Understanding every component before reaching for a framework.
- Setting up your development environment with all required tools and API keys.
MODULE 2 — Vector Databases for RAG
5 hours- How embedding models convert text into high-dimensional vectors that capture semantic meaning.
- Choosing the right embedding model: OpenAI, Cohere, open-source options like BGE and E5.
- Dimensionality, storage costs, and the trade-off between model size and retrieval quality.
- Flat indexes vs approximate nearest neighbor: HNSW, IVF, and PQ explained without heavy math.
- How approximate search trades accuracy for speed and when the trade-off is worth it.
- Configuring index parameters: ef_construction, M, and probes for optimal recall-latency balance.
- Setting up managed vector databases with Pinecone and self-hosted options with Qdrant.
- Upserting, querying, filtering, and namespace management in each database.
- Choosing between managed and self-hosted based on cost, latency, and control needs.
- Using PostgreSQL with pgvector for RAG systems that need relational data alongside vectors.
- Hybrid queries: combining vector similarity with SQL filters for metadata-aware retrieval.
- When pgvector outperforms dedicated vector databases for small-to-medium datasets.
MODULE 3 — LangChain for RAG
6 hours- Document loaders, splitters, embeddings, vector stores, and retrievers in LangChain.
- The LCEL (LangChain Expression Language) pipeline syntax for composing RAG chains.
- When LangChain's abstractions help and when they hide important details.
- Creating a conversational RAG chain with memory, source citations, and streaming output.
- Adding retrieval callbacks, custom prompt templates, and output parsers.
- Handling edge cases: empty retrieval results, low-confidence matches, and context overflow.
- Using LangSmith to trace every step of your RAG pipeline for debugging and optimization.
- Viewing token usage, latency, and retrieval scores at each stage.
- Building evaluation datasets in LangSmith and running automated evaluations.
- Multi-vector retrieval with parent-document retrievers for long documents.
- Ensemble retrievers combining BM25 and vector search for hybrid retrieval.
- Self-querying retrievers that use the LLM to generate structured metadata filters.
MODULE 4 — LlamaIndex for RAG
6 hours- Documents, nodes, indices, and query engines — LlamaIndex's data-centric abstractions.
- How LlamaIndex's approach differs from LangChain: data-first vs chain-first design.
- Choosing between LangChain and LlamaIndex based on your project requirements.
- Creating a VectorStoreIndex from documents and querying with a query engine.
- Customizing retrievers, response synthesizers, and prompt templates.
- Using LlamaIndex's response modes: compact, tree_summarize, and refine.
- Hierarchical node structures with auto-merging retrieval for long documents.
- Document summaries and metadata for filtered, context-aware retrieval.
- Building knowledge graphs with LlamaIndex's KnowledgeGraphIndex.
- Using LlamaIndex's evaluation modules for faithfulness and relevance scoring.
- Integrating with Arize Phoenix and OpenTelemetry for production observability.
- Building a feedback loop: evaluation results driving chunking and retrieval improvements.
MODULE 5 — Advanced RAG Techniques
7 hours- Fixed-size chunking: simple but lossy — when it works and when it fails.
- Semantic chunking and sentence-based splitting for coherent retrieval units.
- Recursive splitting, overlap strategies, and parent-child chunk relationships.
- Query rewriting: using the LLM to rephrase user queries for better retrieval.
- HyDE (Hypothetical Document Embeddings): generating synthetic answers to improve search.
- Multi-query retrieval: generating multiple query variants and merging results.
- Combining dense vector search with sparse BM25 keyword search for hybrid retrieval.
- Using cross-encoder rerankers like Cohere Rerank and BGE-Reranker to boost precision.
- Reciprocal rank fusion and score normalization for merging multiple retrieval sources.
- Post-retrieval compression: extracting only relevant passages from retrieved documents.
- LLM-based filtering: using the model to discard irrelevant context before generation.
- Embedding-based filtering: using cosine similarity thresholds to prune low-relevance chunks.
- Building knowledge graphs from documents using entity extraction and relationship mapping.
- Graph RAG: combining vector retrieval with graph traversal for multi-hop reasoning.
- When graph RAG outperforms flat vector retrieval for complex, relational questions.
MODULE 6 — RAGAS Evaluation
5 hours- Faithfulness: does the answer stick to the retrieved context without hallucination?
- Answer relevance: does the answer actually address the question that was asked?
- Context precision and recall: are the retrieved documents relevant and complete?
- Creating golden datasets with questions, reference answers, and relevant contexts.
- Synthetic data generation: using LLMs to create evaluation datasets from your documents.
- Human annotation strategies for building high-quality ground truth.
- Installing and configuring RAGAS with your RAG pipeline outputs.
- Interpreting metric scores and identifying which pipeline stage needs improvement.
- Running evaluations in CI/CD to catch regressions before deployment.
- Building custom evaluators for domain-specific criteria like legal accuracy or code correctness.
- Using LLM-as-judge with custom rubrics for qualitative evaluation.
- A/B evaluation: comparing two RAG configurations with statistical significance testing.
MODULE 7 — Multimodal RAG
5 hours- Using CLIP and other multimodal embedding models to create image vectors.
- Extracting and embedding tables from PDFs for structured data retrieval.
- Building a unified vector store that handles text, images, and tables together.
- Retrieving mixed-format context and passing images to vision-language models.
- Using GPT-4o and Gemini 1.5 for multimodal answer generation with image references.
- Handling page-level retrieval for documents with interleaved text and images.
- Using tools like Unstructured and LayoutLM to parse complex documents with figures and tables.
- Maintaining reading order and spatial relationships when chunking multimodal documents.
- Building a document processing pipeline that handles PDFs, slides, and web pages.
- Creating a RAG system that answers questions about charts, diagrams, and screenshots.
- Implementing citation that links back to the specific image or table in the source document.
- Evaluating multimodal RAG with adapted RAGAS metrics and human evaluation.
MODULE 8 — Agentic RAG
6 hours- Why static RAG pipelines fail on complex, multi-step questions that need iterative reasoning.
- Agentic RAG: the LLM decides when to retrieve, what to search for, and when it has enough context.
- The ReAct pattern applied to retrieval: reasoning, acting, and observing in a loop.
- Implementing a retrieval tool that the agent calls when it needs more information.
- Designing the agent's system prompt to guide when and how to use retrieval.
- Handling multi-hop questions that require multiple retrieval rounds with query refinement.
- Specialized agents for different knowledge domains with a router agent for query dispatch.
- Building a planner agent that decomposes complex questions into sub-queries for retrieval agents.
- Coordinating multiple agents with shared memory and conflict resolution.
- Implementing self-correction: the agent evaluates its own answer and retrieves more if confidence is low.
- Adaptive retrieval: choosing between vector search, graph traversal, or SQL based on query type.
- Building guardrails: the agent refuses to answer when retrieval quality is insufficient.
MODULE 9 — Production RAG Deployment
6 hours- Building a FastAPI service for RAG with async endpoints, streaming responses, and connection pooling.
- Scaling vector databases: sharding, replication, and read replicas for high-throughput queries.
- Load testing your RAG service and identifying bottlenecks in retrieval and generation.
- Semantic caching: storing and reusing answers for similar queries to reduce API costs.
- Embedding caching: avoiding redundant embedding computations for repeated queries.
- Model routing: using cheap models for simple queries and expensive models only when needed.
- Logging every query, retrieved context, and generated answer for audit and debugging.
- Setting up dashboards for retrieval quality, generation latency, and cost per query.
- Alerting on quality degradation, hallucination spikes, and retrieval failures.
- Implementing document-level access control so users only retrieve documents they can see.
- Preventing prompt injection in retrieved documents that could hijack the generation model.
- Data privacy: redacting PII from retrieved context and generated answers.
MODULE 10 — Capstone Projects
6 hours- Build a RAG chatbot that answers questions from a corpus of 100+ PDF documents.
- Implement semantic chunking, hybrid search, and source citation.
- Deploy with a web UI and evaluate with RAGAS.
- Build a multi-tenant RAG system with document-level access control and metadata filtering.
- Implement ingestion pipelines for multiple data sources: Confluence, Slack, and Google Drive.
- Add semantic caching and cost monitoring for production scale.
- Build a RAG system that retrieves and reasons over text, images, and tables from research papers.
- Implement document layout analysis and multimodal embedding.
- Create a UI that displays cited figures and tables alongside answers.
- Build a ReAct RAG agent that iteratively retrieves information to resolve complex support tickets.
- Implement multi-hop reasoning with query refinement and self-correction.
- Add guardrails for low-confidence answers and human escalation.
- Build a knowledge graph from documents and implement graph RAG for multi-hop questions.
- Combine vector retrieval with graph traversal for relational reasoning.
- Visualize the knowledge graph and retrieval paths in a web UI.
- Build a complete RAG platform with ingestion, retrieval, generation, evaluation, and monitoring.
- Implement A/B testing for retrieval configurations and automated RAGAS evaluation.
- Deploy with Docker, observability, and auto-scaling.
Real-World Projects & Portfolio Outcomes
6 portfolio-worthy projects that prove your skills to employers.
Document Q&A Chatbot
Build a RAG chatbot that answers questions from 100+ PDF documents with semantic chunking, hybrid search, source citation, and RAGAS evaluation.
Enterprise Knowledge Base
Build a multi-tenant RAG system with document-level access control, ingestion from Confluence and Slack, semantic caching, and cost monitoring.
Multimodal Research Assistant
Build a RAG system that retrieves and reasons over text, images, and tables from research papers with document layout analysis and multimodal embeddings.
Agentic RAG Support System
Build a ReAct RAG agent that iteratively retrieves information to resolve complex support tickets with multi-hop reasoning and self-correction.
Graph RAG Knowledge Explorer
Build a knowledge graph from documents and implement graph RAG for multi-hop relational questions with vector-graph hybrid retrieval.
Production RAG Platform
Build a complete RAG platform with ingestion, retrieval, generation, RAGAS evaluation, A/B testing, monitoring, and Docker deployment.
Tools, Technologies & Models Covered
LLM Models Covered
| GPT-4o | OpenAI | Generation, multimodal RAG, agentic reasoning, evaluation |
|---|---|---|
| GPT-4o mini | OpenAI | Cost-effective generation, query rewriting, classification |
| Claude 3.5 Sonnet | Anthropic | Long-context RAG, nuanced generation, citation |
| Gemini 1.5 Pro | Massive context RAG, multimodal retrieval, response schemas | |
| text-embedding-3-large | OpenAI | High-quality embeddings for production retrieval |
| text-embedding-3-small | OpenAI | Cost-effective embeddings for high-volume systems |
| Cohere embed v3 | Cohere | Multilingual embeddings, enterprise deployment |
| BGE-large-en | open-source | Self-hosted embeddings, no API costs, fine-tunable |
Tools & Frameworks Covered
| LangChain | RAG pipeline orchestration, chains, agents | Deep — all modules and patterns |
|---|---|---|
| LlamaIndex | Data-centric RAG, advanced indexing, evaluation | Deep — all index types and query engines |
| Pinecone | Managed vector database for production RAG | Intermediate — upsert, query, filter, namespace |
| Qdrant | Self-hosted or managed vector database | Intermediate — payload filtering, quantization |
| pgvector | PostgreSQL vector extension for in-database RAG | Intermediate — hybrid queries, indexing |
| RAGAS | RAG evaluation framework with faithfulness and relevance metrics | Deep — custom metrics and CI integration |
| Cohere Rerank | Cross-encoder reranking for precision boost | Intermediate — API integration |
| Unstructured | Document parsing for PDFs, images, and complex layouts | Intermediate — layout analysis |
Book a Free Counselling Call
Not sure which course is right for you? Book a free 1:1 counselling call with our AI training advisors.
Book Free Counselling Call →Your Instructor
Dr. Priya Sharma
Senior AI Architect & Former Lead RAG Engineer at a top AI infrastructure company
10+ years in search and information retrieval, 3+ years building production RAG systems
Architected RAG systems processing 10M+ documents for enterprise knowledge bases and customer support
Trained 3,000+ engineers in RAG architecture through workshops and conference talks
A RAG system is only as good as its weakest stage. Optimize retrieval before generation, evaluate before deploying, and measure everything in production.
Course Team & Curriculum Design
This course is co-developed with a team of RAG engineers and retrieval scientists. Our advanced retrieval module is co-authored by a former search infrastructure lead, our evaluation module by an ML research scientist specializing in LLM evaluation, and our production deployment module by a platform engineer who has shipped RAG systems serving millions of queries.
Learning Path & Prerequisites
Prerequisites
- Completion of Prompt Engineering Pro Course or equivalent experience with LLM prompting and API usage.
- Intermediate Python programming — you should be comfortable with async, classes, and API calls.
- Basic understanding of databases and SQL — familiarity with PostgreSQL is a plus.
- Familiarity with REST APIs and basic web development for project deployment.
Recommended Learning Paths
RAG Engineer: From Foundations to Production
- Start with Module 1 to understand the RAG pipeline and build your first system from scratch.
- Complete Module 2 to master vector databases and embedding models for retrieval.
- Learn both LangChain (Module 3) and LlamaIndex (Module 4) to choose the right framework.
- Master advanced techniques in Module 5 — this is where most RAG quality gains come from.
- Learn evaluation in Module 6 so you can measure and prove your system's quality.
- Explore multimodal (Module 7) and agentic RAG (Module 8) for advanced use cases.
- Finish with production deployment (Module 9) and capstone projects (Module 10).
Production RAG in 4 Weeks
- Review Module 1 if you are new to RAG, then jump to Module 2 for vector database setup.
- Pick one framework — LangChain or LlamaIndex — and complete that module thoroughly.
- Focus heavily on Module 5 for advanced retrieval — this is the highest-ROI module.
- Implement RAGAS evaluation from Module 6 immediately — measure before you optimize.
- Go straight to Module 9 for production deployment patterns.
- Complete the capstone project that matches your production use case.
What Comes After This Course
Pricing & Enrollment
Choose the plan that fits your learning goals. All plans include a 7-day money-back guarantee.
| Early Bird | ₹3,999 | Full course access, 6 project reviews, community access, certificate — limited time offer |
|---|---|---|
| Standard | ₹5,999 | Full course access, 6 project reviews, community access, certificate, 3 months instructor Q&A |
| Premium | ₹9,999 | Everything in Standard plus 1-on-1 mentorship, career guidance, lifetime updates, priority support |
What Is Included
- 55+ hours of HD video lessons with downloadable transcripts
- 10 modules with hands-on labs and code-along exercises
- 6 capstone projects with personalized feedback
- Access to our private community of RAG engineers
- Downloadable code templates for production RAG pipelines
- Certificate of completion — RAG Masterclass
- 3 months of instructor Q&A access (Standard and Premium)
- Lifetime course updates as frameworks and models evolve
30-day money-back guarantee. If you complete the first 3 modules and do not find the course valuable, we will refund your full payment — no questions asked.
Frequently Asked Questions
Do I need to know prompt engineering before taking this course?
Yes. We assume you know how to use LLM APIs and write effective prompts. If you are new to LLMs, complete Prompt Engineering Pro first. RAG builds on prompting skills — it does not teach them from scratch.
Which vector database should I use?
The course covers Pinecone, Qdrant, Weaviate, and pgvector so you can make an informed choice. For learning, we recommend Pinecone (managed) or Qdrant (self-hosted). For production, the choice depends on your scale, budget, and infrastructure preferences — we provide a decision framework.
Do I need both LangChain and LlamaIndex?
No. We teach both so you can choose. Most students pick one based on their project needs. LangChain is better for complex agent workflows; LlamaIndex is better for data-heavy RAG. The course helps you decide which fits your use case.
How much will API costs be during the course?
Budget ₹1,500-3,000 for API credits (OpenAI, Anthropic, or Google) and vector database usage during the course. We show cost optimization techniques including semantic caching and model routing to keep costs down.
Is this course updated when new RAG techniques emerge?
Yes. We update within 30 days of major framework releases or significant new techniques. The last update was July 2026, covering the latest LangChain and LlamaIndex versions, RAGAS updates, and agentic RAG patterns. Lifetime updates are included.
Can I use open-source models instead of paid APIs?
Yes. We show how to use open-source embedding models like BGE and E5, and open-source LLMs like Llama 3 for generation. This eliminates API costs but requires more infrastructure setup. We cover both approaches.
What is the difference between this and the Vector Databases course?
This course covers the full RAG pipeline including retrieval, generation, evaluation, and deployment. The Vector Databases course goes deeper into database internals, indexing, performance tuning, and non-RAG use cases like recommendation systems. They complement each other.
Will this help me build RAG for my specific industry?
The techniques are industry-agnostic. The capstone projects cover document Q&A, enterprise knowledge bases, research assistants, and support systems — patterns that apply across industries. You will adapt the frameworks to your domain.
What to Learn Next
Continue your AI learning journey with these recommended courses.
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