Reference
Prompt Engineering Glossary
Every term you need, with working example prompts and practical notes on when each technique earns its keep.
10 terms in Applied
Agentic RAG
RAG where the model doesn't passively consume retrieved context, but actively decides what to retrieve, iterates queries, and judges retrieval quality before answering.
AppliedChunking
Splitting a document into smaller pieces before embedding and indexing -- so retrieval returns coherent, focused passages instead of entire docs.
AppliedEvals
Automated test suites for LLM outputs -- measuring accuracy, safety, or task-specific quality metrics against labeled inputs, so you know when a prompt or model change helps or regresses.
AppliedGrounding
Constraining an LLM's response to information provided in the prompt -- typically retrieved documents -- rather than the model's parametric (trained-in) knowledge.
AppliedHallucination
An LLM confidently producing content that sounds plausible but is factually wrong, fabricated, or ungrounded -- a fundamental failure mode of all current generative models.
AppliedHybrid Search
Retrieval that combines sparse (keyword, BM25) and dense (embedding, semantic) signals, typically fusing their rankings into one list -- beats either alone on most real corpora.
AppliedPersona Prompting
Instructing the model to adopt a specific role, expertise level, or character -- typically via the system prompt -- to shape tone, depth, and domain framing.
AppliedReranker
A model that takes a candidate list of retrieved documents and a query, and produces a more accurate ranking -- applied after initial retrieval (BM25, vector, or hybrid) as a second pass.
AppliedRetrieval-Augmented Generation (RAG)
A pattern where relevant context is retrieved from a knowledge base (vector store, search index, graph) and injected into the prompt before the model generates its answer.
AppliedSemantic Search
Search that matches on meaning (via embeddings) rather than exact keywords -- finding relevant documents even when they share no surface words with the query.