#RAG

Overcoming the limitations of pure vector database search in RAG pipelines. An honest guide to implementing keyword-based BM25, combining results with Reciprocal Rank Fusion (RRF), and applying Cohere Rerank to boost context quality.

LLMs don't know what they weren't trained on. Here's how RAG fixes that — walking through the complete pipeline from document ingestion to chunking, embedding, vector storage, retrieval, and generation with real Python and TypeScript examples.

How do computers understand that 'King' - 'Man' + 'Woman' = 'Queen'? We dive deep into the evolution of NLP embeddings, from One-Hot Encoding to Word2Vec and Transformer-based models. Learn about Vector Databases, Cosine Similarity math, and how RAG (Retrieval-Augmented Generation) is reshaping modern AI applications.

My AI chatbot was hallucinating wild answers to customers. Here's how I implemented RAG (Retrieval-Augmented Generation) to fix it, covering Vector DBs, Embeddings, and Hybrid Search.

Understanding AI, Machine Learning, Deep Learning, and Generative AI. Deep dive into Transformer architecture, RAG vs Fine-tuning, Ethical AI, and a practical roadmap for developers transitioning to AI Engineering.