RAG workflow breakdown #RAG #vectorsearch #AIengineering

Description

RAG is presented as a core skill for AI engineers, with a concise breakdown of how retrieval-augmented generation works under the hood. The process starts by turning source material such as PDFs, webpages, and Slack messages into semantic chunks, converting those chunks into embeddings, and storing them in a vector database for fast retrieval. When a user submits a query, the same embedding process is applied to the question, and similarity search finds the most relevant chunks using cosine similarity. Those retrieved passages are then provided to the LLM as context, helping it generate more accurate, grounded answers. The key insight is that RAG is conceptually simple at its foundation, even though production-grade implementations become much more complex. For AI engineers, understanding embeddings, chunking, vector databases, and retrieval is essential for building useful, context-aware AI systems. #RAG #RetrievalAugmentedGeneration #AIEngineering #LLM #Embeddings #VectorDatabase #Pinecone #Weaviate #pgVector #CosineSimilarity #SemanticSearch #Chunking #GenerativeAI #AIInfrastructure #MachineLearning