Visualpath’s RAG Training is a practical, hands-on program designed to help professionals understand, build, evaluate, test, and deploy Retrieval-Augmented Generation applications. The course progresses from RAG fundamentals to advanced implementation, covering LangChain, document processing, vector databases, retrieval, advanced RAG architectures, evaluation, regression testing, AWS deployment, and LangSmith monitoring. This RAG Course is designed for learners who want practical experience building production-oriented RAG applications rather than learning only theoretical concepts. Through coding exercises, hands-on projects, and real-world scenarios, learners develop the skills needed to create reliable applications that combine large language models with external knowledge sources.
RAG Evaluation course covers Retrieval-Augmented Generation (RAG) a powerful technique that combines large language models with external knowledge retrieval. Students will learn to build production-ready RAG systems using LangChain, manage vector databases, evaluate model performance, and deploy solutions on cloud platforms with real-time monitoring.
By the end of this course, students will be able to:
This is a comprehensive, project-based course designed to provide practical hands-on experience with RAG systems. Each module builds upon previous concepts, progressing from fundamentals to advanced production-ready implementations. Students are expected to actively participate in coding exercises and real-world project scenarios throughout the course.
Retrieval-Augmented Generation Course , commonly known as RAG, combines information retrieval with large language models. Instead of relying only on the knowledge available within a language model, a RAG application retrieves relevant information from an external knowledge source and uses that information to generate a response. A typical RAG workflow involves loading documents, splitting content into useful chunks, generating embeddings, storing information in a vector database, retrieving relevant content, and passing the retrieved context to a language model. Visualpath’s Retrieval Augmented Generation Course follows this complete workflow and then moves into advanced retrieval, evaluation, testing, deployment, and production monitoring.
Build practical RAG skills from fundamentals to advanced implementation. Learn RAG architecture, LangChain, document processing, text splitting, vector databases, embeddings, retrievers, Naive RAG, Advanced RAG, Self-RAG, and Corrective RAG. The course also covers evaluation metrics, online and offline evaluation, regression testing, AWS deployment, LangSmith monitoring, and post-deployment evaluation.
RAG with LangChain is an important part of the learning path. LangChain provides tools and components that can be used to build applications around large language models, document retrieval, and external knowledge sources. Learners work with document loaders, text splitting, retrievers, vector databases, and RAG pipelines. The course focuses on understanding how these components work together rather than treating them as isolated technologies. The practical exercises help learners understand how to create retrieval workflows and progressively improve the quality of generated responses.
Basic retrieval is only the starting point. The course introduces advanced approaches that can improve retrieval quality and application reliability. Learners explore multi-stage retrieval, query expansion, advanced generation techniques, Self-RAG, and Corrective RAG. These approaches help demonstrate how RAG applications can be designed to retrieve better information, identify problems, and refine responses. The RAG Application Development component focuses on turning these concepts into working applications. Learners build RAG systems from the ground up and gain experience with the components required for practical implementations.
Learn how to evaluate RAG applications using practical performance metrics such as precision, recall, NDCG, and relevance scoring. The training covers both offline benchmarking and online evaluation approaches, including A/B testing and user evaluation. Learners also explore regression testing and post-deployment evaluation to monitor quality and identify performance changes across RAG application updates.
Reliable RAG applications need continuous testing and evaluation. The curriculum covers both offline benchmarking and online evaluation approaches, including A/B testing and user evaluation methodologies. The course also introduces RAG Testing and Evaluation practices that can help identify quality issues before and after deployment. Regression testing is another important component. Learners understand how to establish regression test suites and monitor changes so that application updates do not unintentionally reduce response quality. The RAG Regression Testing module adds a practical quality-assurance perspective to RAG application development.
Production RAG applications require visibility into how systems behave over time. The course introduces LangSmith for tracking, dashboards, observability, monitoring, and evaluation. Through LangSmith RAG Evaluation, learners understand how production systems can be monitored and evaluated continuously. The curriculum also covers post-deployment online evaluation and feedback loops. This helps connect development and testing with real-world production monitoring.
Visualpath focuses on practical, instructor-led learning with hands-on exercises and real-world project scenarios. The RAG curriculum is structured to move from fundamentals to advanced implementation, evaluation, testing, and deployment. Visualpath offers RAG Online Training for learners who want to participate remotely, along with customized online and corporate training globally for organizations and technical teams. Corporate programs can be adapted to business requirements, technology stacks, project needs, schedules, and team skill levels. The learning approach combines technical concepts with practical implementation so that learners can work with the frameworks and tools covered in the curriculum.
