How Generative AI and LLMs Work: Tokens, RAG, and Evaluation
Learn how generative AI and LLMs use tokens, context windows, Transformers, retrieval, RAG, and evaluation to produce and assess outputs in practice.
Generative AI and Large Language Models
Explore how generative models produce responses, retrieve external context, and still fail. The focus is technical enough to be useful without turning into model-development documentation.
4 articles
A practical route through the LLM system
Four guides separating model mechanics, knowledge retrieval, and output risk.
- 01
how tokens, context, retrieval, and evaluation work together — Begin with the end-to-end system before examining individual components.
- 02
the large language model as a language engine — Understand the definition, training, abilities, product relationships, and limits.
- 03
RAG as a connection to external sources — Explore retrieval pipelines, source governance, citations, and evaluation.
- 04
why an LLM can produce a false answer — Separate factuality from groundedness and match controls to consequence.
Start here
Topic map
Language model foundations
Understand the LLM as a model that processes tokens, uses context, and generates language.
- What Is a Large Language Model? How LLMs Work and Where They Fall Short
Learn what a large language model is, how an LLM works at a practical level, what it can do, and which limitations businesses need to understand.
Retrieval and grounding
Connect model capability with documents, data, citations, and source governance.
- What Is RAG? How Retrieval-Augmented Generation Works
Learn what retrieval-augmented generation is, how a RAG pipeline works, when it helps, and which risks still require evaluation.
Failure modes and evaluation
Understand why fluent output can still be wrong and how that risk is tested.
- Why Do Large Language Models Hallucinate? Causes, Risks, and Mitigation
Understand why LLMs hallucinate, how factual and groundedness failures differ, and which controls can reduce and evaluate the risk.
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