RAG Engineer

LangChain Course

The LangChain Course Basic to Advance provides learners with a strong foundation in building Retrieval-Augmented Generation (RAG) applications using the LangChain framework. Participants learn prompt templates, document loaders, text splitters, embeddings, retrievers, chains, output parsers, agents, memory, and workflow orchestration. Through hands-on projects and real-world business scenarios, the course develops practical skills to create intelligent AI applications that retrieve, process, and generate context-aware responses from enterprise knowledge sources.

LlamaIndex Course

The LlamaIndex Course Basic to Advance equips learners with the skills to build efficient knowledge retrieval systems for large language model applications. Participants explore document ingestion, indexing strategies, data connectors, query engines, retrieval optimization, metadata filtering, hybrid search, evaluation techniques, and integration with vector databases and language models. The course focuses on developing scalable RAG solutions through practical implementation and enterprise use cases.

Pinecone Course

The Pinecone Course Basic to Advance provides comprehensive training in managing vector databases for semantic search and Retrieval-Augmented Generation applications. Learners master vector indexing, embedding storage, similarity search, namespaces, metadata filtering, scalability, security, performance optimization, and LangChain integration. Through hands-on projects, the course enables participants to build fast, reliable, and production-ready retrieval systems.

Weaviate Course

The Weaviate Course Basic to Advance teaches learners how to build AI-native vector search applications using Weaviate. Participants learn schema design, object management, vector indexing, semantic search, hybrid search, GraphQL queries, metadata filtering, authentication, clustering, and performance optimization. The course develops practical expertise in designing scalable vector database solutions for enterprise AI applications.

ChromaDB Course

The ChromaDB Course Basic to Advance focuses on developing lightweight vector database solutions for Retrieval-Augmented Generation workflows. Learners explore document collections, embeddings management, indexing, similarity search, metadata handling, persistence, retrieval optimization, local deployment, and integration with LangChain and LlamaIndex. Through project-based learning, the course prepares participants to create efficient semantic search systems for AI-powered applications.

FAISS Course

The FAISS Course Basic to Advance equips learners with advanced skills in high-performance vector similarity search using Facebook AI Similarity Search (FAISS). Participants learn vector indexing techniques, nearest neighbor search, clustering algorithms, embedding optimization, large-scale search strategies, performance tuning, and integration with modern RAG pipelines. The course emphasizes building scalable semantic retrieval systems through practical implementation and enterprise datasets.

OpenAI Embeddings Course

The OpenAI Embeddings Course Basic to Advance provides comprehensive training in generating and utilizing vector embeddings for semantic search and Retrieval-Augmented Generation applications. Participants learn embedding generation, vector representation, similarity measurement, chunking strategies, retrieval optimization, embedding evaluation, integration with vector databases, search pipelines, and performance best practices. Through hands-on projects, the course prepares learners to build accurate, context-aware, and production-ready RAG systems powered by OpenAI embedding models.