Production Rag Engineering: Build, Evaluate & Deploy Retrieval-Augmented Generation Systems
Looking for professional Retrieval Augmented Generation (RAG) & training in Gurugram Our comprehensive course at Blazingminds Learning is designed for students, working professionals, and business owners who want to gain practical expertise in Artificial Intelligence, Model Evaluation, Embeddings, Retrieval-Augmented Generation, Application Security, Large Language Modeling, System Monitoring, Fine-tuning, LLM Application, Vector databases. The training covers industry-relevant concepts, hands-on projects, real-world case studies, and certification preparation to help learners build job-ready skills.
With expert trainers, flexible learning schedules, and placement assistance, our Retrieval Augmented Generation (RAG) course in Gurugram helps participants stay competitive in today's job market. Whether you are a beginner or an experienced professional looking to upskill, this program provides the knowledge and practical experience needed to succeed.
Enroll today in the leading Retrieval Augmented Generation (RAG) training institute in Gurugram and take the next step toward your career goals.
Understand and implement key concepts of Limitations of standalone LLMs to build, scale, and integrate secure FastMCP tools and servers.
Understand and implement key concepts of Knowledge cutoff to build, scale, and integrate secure FastMCP tools and servers.
Understand and implement key concepts of Hallucination to build, scale, and integrate secure FastMCP tools and servers.
Understand and implement key concepts of Private enterprise data to build, scale, and integrate secure FastMCP tools and servers.
Understand and implement key concepts of Domain-specific knowledge to build, scale, and integrate secure FastMCP tools and servers.
Understand and implement key concepts of Why RAG exists to build, scale, and integrate secure FastMCP tools and servers.
Understand and implement key concepts of Definition of Retrieval-Augmented Generation to build, scale, and integrate secure FastMCP tools and servers.
Understand and implement key concepts of Parametric vs non-parametric knowledge to build, scale, and integrate secure FastMCP tools and servers.
Understand and implement key concepts of RAG vs prompt engineering to build, scale, and integrate secure FastMCP tools and servers.
Understand and implement key concepts of RAG vs fine-tuning to build, scale, and integrate secure FastMCP tools and servers.
Understand and implement key concepts of RAG vs long-context prompting to build, scale, and integrate secure FastMCP tools and servers.
Understand and implement key concepts of When RAG is appropriate to build, scale, and integrate secure FastMCP tools and servers.
Understand and implement key concepts of When RAG is not appropriate to build, scale, and integrate secure FastMCP tools and servers.
Understand and implement key concepts of Notebook prototype vs production application to build, scale, and integrate secure FastMCP tools and servers.
Understand and implement key concepts of Separation of ingestion and query pipelines to build, scale, and integrate secure FastMCP tools and servers.
Understand and implement key concepts of Configuration management to build, scale, and integrate secure FastMCP tools and servers.
Understand and implement key concepts of Environment variables to build, scale, and integrate secure FastMCP tools and servers.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Dependency management techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Logging structure techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Model/provider abstraction techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Retriever abstraction techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Vector-store abstraction techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Service layers techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging RAG ingestion architecture techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Batch ingestion techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Incremental ingestion techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Source connectors techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging File-based ingestion techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging API ingestion techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Database ingestion techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Change detection techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Data versioning techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Document IDs techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Deduplication techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Document hashing techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Re-indexing techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Upserts techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Delete/update handling techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Document Parsing techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Text Cleaning techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Metadata Engineering techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Why Chunking? techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Strategies techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Chunk Parameters techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Advance Pattern techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Chunk Quality Evaluation techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Embedding techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Similarity Functions techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Embedding Model Selection techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Production Considerations techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Vector store vs vector database techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Vector index techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Exact nearest neighbor techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Approximate nearest neighbor techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging ANN concepts techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging HNSW techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging IVF concepts techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Similarity metrics techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Top-K retrieval techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Lexical Retrieval techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Dense Retrieval techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Retrieval Parameters techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Retrieval Failure Modes techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Retrieval Evaluation techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Sparse retrieval techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Dense retrieval techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Hybrid retrieval techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Weighted score fusion techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Reciprocal Rank Fusion techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Metadata filtering techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Filter-before-search techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Filter-after-search techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Why user queries are poor retrieval queries techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Query normalization techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Query rewriting techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Query expansion techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Acronym expansion techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Spelling normalization techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Domain terminology techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Metadata extraction from queries techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Query decomposition concepts techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Multi-query retrieval techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging HyDE concepts techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Why initial retrieval is imperfect techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Retrieve-many / rerank-few techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Bi-encoder vs cross-encoder techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Cross-encoder reranking techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging LLM reranking concepts techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Diversity-aware ranking techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Duplicate suppression techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Maximal Marginal Relevance techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Score normalization techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Context Engineering techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Grounded Generation techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Context Failure Modes techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Build an Evaluation Dataset techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Retrieval Evaluation techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Generation Evaluation techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging End-to-End Evaluation techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Evaluation Approaches techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Framework Awareness techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Regression Testing techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Threats techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Retrieval Authorization techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Latency Breakdown techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Cost techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Tradeoffs techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Production Architecture techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Monitoring techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Distributed Tracing Concepts techniques and architectures.
Build, optimize, and deploy production-grade enterprise RAG systems leveraging Production Capstone Project :Enterprise Knowledge Assistant techniques and architectures.
In case you are thinking of beginning your IT career, Retrieval Augmented Generation (RAG) is the best course program to start and our is structured in such a way that you will be job ready on the first day after course completion.
Manish Kumar
"The course provides practical exposure to retrieval-augmented generation, embeddings, chunking, vector databases, retrieval, reranking, and LLM integration. The trainer explains architecture clearly and uses practical examples, helping learners understand how to build grounded AI applications."
Amit Sharma
"Learners build practical understanding of document ingestion, chunking, embeddings, vector search, retrieval, and response generation. Trainer demonstrations connect individual components into complete pipelines, providing valuable experience for developing production-oriented knowledge assistants and enterprise AI solutions."
Dipesh Pandey
"A practical program covering essential RAG architecture and implementation concepts. Learners understand how retrieval improves factual grounding and reduces unsupported responses. Hands-on exercises and trainer expertise provide strong preparation for enterprise AI and knowledge-management projects."
Fateh Singh
"The instructor explains RAG components systematically and demonstrates complete retrieval workflows. Learners gain practical exposure to embeddings, vector stores, chunking, and prompt integration. Project-oriented teaching makes complex architecture concepts easier to apply professionally."
Niharika Singh
"The course delivers practical RAG development skills relevant to enterprise GenAI applications. Strong technical instruction, implementation exercises, and architecture discussions help learners build confidence designing document-based AI assistants and scalable retrieval workflows."
Yes, course is available in both modes.
Yes, shareable digital certificate.
Yes, Few basis , we allow candidates to pay fee in parts.
5 major minor portfolio projects.
Yes, we provide you the assured placement. we have a dedicated team for placement assistance.
Career outcomes will be added soon.
Shareable on LinkedIn & Resume
1-on-1 Mentorship: Dedicated professional mentors guide you through every challenge.
Peer Learning Community: Join 1k+ learners in weekly code reviews.
Confidence Booster: Project-based learning with real-world simulations.
TechCorp • Remote
InnovateAI • Bangalore
ScaleUp • Hybrid
Praveen Y
01:00 PM - 01:00 PM
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*All events are recorded and available for enrolled students
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