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RAG is the dominant architecture for production AI applications that need to answer questions about specific documents, recent events, or private data. Understanding it completely — including its fail
Developers often focus on which LLM to use or which embedding model is best. In practice, chunking strategy has a larger impact on RAG quality than either of those choices. A mediocre LLM with good ch
Vector databases are purpose-built for one operation that general-purpose databases handle poorly: finding the k most similar vectors to a query vector. Understanding why this requires specialized inf
Semantic search is the retrieval mechanism in RAG. Getting it right is the difference between an AI assistant that finds the correct information and one that retrieves garbage and hands it to the LLM.
Before you can build a RAG system, design a semantic search engine, or understand why vector databases exist, you need to understand embeddings. This is the concept that makes everything else work. Wh
The term AI Engineer is used loosely. Let us define it precisely so you know exactly what you are learning to become. Three Roles, Three Different Jobs ML Engineer: Responsible for creating machine
