STAGE 01
GenAI Foundations
Understand Generative AI, foundation models, LLMs, tokens, context windows and model APIs.
Learn to design and build practical Retrieval-Augmented Generation systems that connect Large Language Models with enterprise documents, knowledge bases, vector databases and external information sources.
Retrieval-Augmented Generation (RAG) is one of the most important application architectures for modern Generative AI. Instead of depending entirely on information encoded inside a language model, RAG applications retrieve relevant information from external knowledge sources and provide that context to the model.
A practical RAG engineer needs to understand the complete pipeline: document ingestion, parsing, chunking, metadata, embeddings, vector indexing, retrieval, reranking, prompt construction and LLM generation.
The learning path therefore goes beyond simply calling an LLM API. It focuses on how to construct reliable retrieval architectures that can work with enterprise policies, manuals, contracts, technical documentation, product information and organizational knowledge.
Advanced topics include hybrid retrieval, metadata filtering, query transformation, reranking, retrieval evaluation, hallucination reduction, access control, observability and production RAG architecture.
Progress from LLM fundamentals to production-grade Retrieval-Augmented Generation systems.
Understand Generative AI, foundation models, LLMs, tokens, context windows and model APIs.
Work with PDFs, documents, websites, databases, structured data and enterprise knowledge repositories.
Extract content, clean documents, preserve metadata and prepare information for retrieval.
Design effective chunking strategies using document structure, semantic boundaries and metadata.
Convert information into vector representations for semantic similarity and retrieval.
Store, index and search embeddings using vector database technologies and similarity search.
Implement semantic search, metadata filtering, hybrid retrieval and query transformation.
Combine retrieved context with prompts and LLMs to generate grounded responses.
Apply evaluation, security, observability, governance and optimization to enterprise RAG applications.
A practical curriculum covering the complete RAG lifecycle from knowledge ingestion to production deployment.
Understand the foundations required before designing Retrieval-Augmented Generation systems.
Understand how retrieval and generation components work together in a RAG application.
Prepare enterprise information for AI retrieval systems.
Learn how document structure affects retrieval quality and downstream LLM responses.
Understand how text is represented numerically for semantic retrieval.
Learn how vector stores support high-performance similarity search.
Build retrieval mechanisms that identify information based on meaning rather than only keyword matching.
Improve retrieval quality using advanced retrieval and search strategies.
Connect ingestion, retrieval and generation into complete AI application workflows.
Design RAG systems around real enterprise knowledge and organizational requirements.
Measure retrieval and generation quality and improve system reliability.
Understand the operational requirements for reliable enterprise RAG systems.
Develop the technical capabilities required to build reliable retrieval-based Generative AI applications.
Integrate language models into retrieval and knowledge-based AI applications.
Process, clean, structure and enrich documents for AI retrieval.
Create and use vector representations for semantic similarity and retrieval.
Store and search embeddings using vector database and indexing technologies.
Build retrieval systems that identify relevant information using semantic similarity.
Improve retrieval precision by ranking candidate documents according to relevance.
Design complete retrieval and generation pipelines for AI applications.
Apply evaluation, monitoring, security and governance to enterprise RAG systems.
Build practical retrieval systems that connect enterprise knowledge with modern language models.
Build a RAG assistant that retrieves information from enterprise documents and generates contextual answers.
Create a document question-answering system using PDF ingestion, chunking, embeddings and retrieval.
Build a semantic search application using embeddings, vector indexing and similarity retrieval.
Develop a knowledge assistant that retrieves relevant company policies before generating responses.
Combine keyword and vector retrieval with filtering and reranking to improve search relevance.
Design an end-to-end RAG system including ingestion, retrieval, generation, evaluation, monitoring and security controls.
Follow the complete RAG engineering sequence from raw enterprise information to grounded AI responses.
RAG engineering skills can support application development, AI architecture and enterprise Generative AI roles.
Design and develop retrieval pipelines, vector search systems and LLM-powered knowledge applications.
Build enterprise applications using foundation models, LLMs, RAG and modern Generative AI architectures.
Develop language-model applications involving context, retrieval, embeddings and evaluation.
Design enterprise AI architectures combining knowledge, models, applications, security and infrastructure.
Build organizational knowledge systems that make enterprise information accessible through AI.
Build and operate the infrastructure supporting enterprise RAG and Generative AI workloads.
Continue your AI Engineering journey through LLMs, Agentic AI, Generative AI and machine learning.
Learn foundation models, Generative AI architectures and modern AI application patterns.
Develop practical skills for building applications around large language models.
Explore AI agents, tool calling, planning and multi-step AI workflows.
Build a strong foundation in machine learning, model development and evaluation.
A RAG course in India can provide professionals with the practical knowledge required to build AI applications that use enterprise and domain-specific information. Retrieval- Augmented Generation has become an important architecture for connecting Large Language Models with external knowledge.
A complete RAG learning path should cover document processing, chunking, embeddings, vector databases, semantic search, retrieval pipelines, LLM integration and evaluation. These components determine how effectively an AI system can locate and use relevant information before generating a response.
Enterprise RAG applications can work with many forms of organizational information including policies, manuals, technical documentation, contracts, product information, knowledge articles and business documents. Appropriate metadata, access controls and retrieval strategies are important when deploying these systems in enterprise environments.
Advanced RAG engineering includes hybrid search, reranking, query transformation, retrieval evaluation, grounded generation, observability, security and production optimization. Practical projects are therefore essential for understanding how a RAG architecture behaves beyond a simple demonstration.
Retrieval-Augmented Generation, or RAG, is an AI architecture that retrieves relevant information from external knowledge sources and provides that context to a language model before generating a response.
RAG allows AI applications to use domain-specific and changing information from external knowledge sources instead of depending only on information contained within a model.
Embeddings are numerical representations of information that capture semantic relationships. They can be stored and searched to identify information that is semantically related to a query.
A vector database stores vector representations such as embeddings and provides similarity-search capabilities for retrieving relevant information.
Semantic search retrieves information according to meaning and contextual similarity rather than relying only on exact keyword matches.
An enterprise RAG system connects organizational knowledge sources with retrieval and language-model components so users can obtain contextual responses from approved business information.
Hybrid search combines multiple retrieval approaches, commonly keyword-based retrieval and vector-based semantic retrieval, to improve the relevance of retrieved information.
Reranking evaluates retrieved candidate documents again and reorders them according to their relevance to the user's query before the final context is provided to the language model.
Yes. Practical projects can include document assistants, enterprise knowledge assistants, semantic search applications, hybrid RAG systems and production-oriented RAG platforms.
Yes. RAG is an important application architecture within modern AI Engineering, particularly for enterprise Generative AI and LLM-based knowledge applications.
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