Tokens, Embeddings, Transformers, RAG: Key AI Concepts Every Developer Should Know

Most developers have used tools like ChatGPT or GitHub Copilot, but building robust AI-powered applications requires understanding the underlying mechanics. LLMs process text as tokens rather than whole words, which directly affects API costs and prompt design decisions. The Transformer architecture, introduced in 2017, revolutionized language processing by using self-attention to analyze relationships between all tokens simultaneously, enabling modern models to maintain context effectively. Embeddings convert text into high-dimensional vectors that capture semantic meaning, allowing applications to retrieve information based on intent rather than exact keyword matches. Retrieval-Augmented Generation (RAG) further enhances AI systems by letting models fetch relevant external documents before generating responses, improving accuracy and reducing hallucinations.