Mar 6
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Welcome to Class 11 of FusionPact’s AI/ML Daily Sessions. In this session, we dive deep into Vector Databases, Embeddings, and Semantic Search, which form the foundation of modern AI systems such as Retrieval-Augmented Generation (RAG), AI assistants, and intelligent search engines. Traditional databases work well for structured data and keyword-based search but fail when dealing with unstructured data like text, images, audio, and videos. Vector databases solve this problem by storing numerical vector embeddings that represent the semantic meaning of data. This class explains: - What vector embeddings are - How embeddings represent semantic meaning - Why vector databases are needed - Difference between traditional databases and vector databases - How semantic search works - Similarity search vs exact keyword search - Cosine similarity, dot product, and Euclidean distance - How vector databases store and retrieve embeddings - Query processing in vector search - Nearest neighbor search and indexing techniques - Role of vector databases as a memory layer - How vector search improves LLM accuracy and reduces hallucinations - Foundations of Retrieval-Augmented Generation (RAG) - Real-world applications: - AI chatbots and assistants - Recommendation systems - Intelligent enterprise search - Challenges in vector databases: - Latency at scale - Large-scale similarity search - Cost considerations Key takeaway: Vector databases are a foundational technology for production-grade AI systems, enabling semantic search, scalable memory, and reliable AI responses. This session is ideal for: - AI & ML students - LLM and RAG developers - Data engineers - Backend and full-stack engineers - Anyone building intelligent AI applications Subscribe to Fusionpact for daily AI/ML sessions focused on real-world AI architectures and systems. #vectordatabases #embeddings #semanticsearch #RAg #llm #fusionpact #DailyAISessions #aiengineering