# RAG glossary

Terms are defined in the sense used by this repository. Similar names can have
different meanings across papers and products; the surrounding corpus, metric,
and implementation still need to be specified.

## A

**Abstention** — Choosing not to provide a factual answer when evidence,
confidence, authority, or policy is insufficient. Measure error among answered
queries and missed useful answers separately.

**ACL (access-control list)** — Principals or groups allowed to access an object.
RAG must propagate ACLs to chunks, embeddings, graph edges, caches, and logs and
enforce them before content crosses a component boundary.

**Adaptive RAG** — A system that routes among retrieval strategies, sources, or
budgets based on the request/state rather than always using one fixed path.

**Agentic RAG** — A stateful policy that plans and executes multiple retrieval,
read, tool, verify, or answer actions under a stopping rule and hard budget.

**ANN (approximate nearest-neighbor search)** — Retrieves likely nearest vectors
without exhaustive comparison, trading recall for latency/memory.

**ANN recall** — Overlap between approximate and exact nearest-neighbor top-k.
It is an index diagnostic, not evidence relevance or answer correctness.

**Answer relevance** — How directly an answer addresses the question. A
relevant answer can still be false or unsupported.

**Answerability** — Whether available evidence is sufficient to answer a
question under the declared policy.

**ASQA** — Ambiguous/long-form QA benchmark often used for attributed generation
and citation evaluation.

**Attribution** — Mapping generated claims to evidence actually supporting them.

**Authority** — Source appropriateness or standing for a claim, distinct from
semantic relevance and entailment.

## B

**BGE** — Family of general/multilingual embedding and reranking models. Exact
model, revision, instructions, dimension, and benchmark slice must be stated.

**Bitemporal data** — Stores valid/event time and system/observed time, enabling
both historical truth and “what the system knew then” queries.

**BM25** — Probabilistic lexical ranking with inverse document frequency,
term-frequency saturation, and length normalization.

**BM25F** — Fielded BM25 that combines title/body/other fields with independent
weights and length normalization.

**Block-Max WAND** — Exact/approximate dynamic-pruning algorithm using score
upper bounds for postings blocks to skip documents unable to enter top-k.

**BRIGHT** — Reasoning-intensive retrieval benchmark where ordinary embedding
leaderboards transfer poorly.

## C

**Calibration** — Mapping a score/confidence to an empirically reliable
probability/risk under a specified distribution.

**Candidate generation** — Fast high-recall first-stage retrieval before an
expensive reranker or selector.

**Canonical document** — Faithful structured representation of source bytes
with alignment/provenance; separate from retrieval-specific normalized views.

**CDC (change-data capture)** — Incremental stream of inserts, updates, and
deletes used to keep indexes synchronized with a source.

**Citation completeness/recall** — Fraction of externally verifiable claims that
have sufficient cited support.

**Citation correctness/precision** — Fraction of citations that actually support
their attached claims.

**Claim** — Atomic externally verifiable proposition extracted from an answer or
source for support evaluation.

**Claim recall** — Fraction of required/reference claims covered by retrieved
evidence or generated output, depending on stage.

**Chunk** — A retrievable unit derived from a source document with stable source
identity and offsets.

**Chunking** — Constructing retrieval units; includes fixed, structural,
semantic, proposition, parent-child, hierarchical, visual, and other policies.

**ColBERT** — Multi-vector late-interaction retriever using query-token to
document-token MaxSim.

**ColPali** — Visual document retriever using text-query/image-patch late
interaction over rendered pages.

**CombMNZ** — Score fusion that multiplies summed normalized scores by number of
retrievers contributing a positive score.

**CombSUM** — Sum of normalized scores across retrieval runs.

**Compression** — Reducing evidence tokens/vectors using extractive,
abstractive, token-level, or latent methods. Must evaluate support preservation.

**Conformal risk/control** — Uses calibration data to provide statistical risk
bounds under explicit assumptions.

**Context precision** — Fraction of retrieved/selected context that is relevant
or supports required claims, depending on definition.

**Context utilization** — Degree to which a generator uses relevant provided
evidence; not identical to faithfulness.

**Contriever** — Unsupervised contrastively trained dense retriever.

**Corrective RAG (CRAG method)** — Grades initial retrieval and refines or
switches search when evidence appears poor. Distinguish from CRAG benchmark.

**CRAG benchmark** — Dynamic/long-tail/complex QA benchmark with web and KG/API
resources, unrelated to the corrective method acronym collision.

**Cross-encoder** — Jointly encodes query and document for high-interaction
ranking; documents cannot be independently pre-indexed by that score.

## D

**Data plane** — Runtime path that processes user requests and evidence, as
opposed to ingestion, control, and evaluation planes.

**DeepCT** — Learned contextual term-impact weighting stored in an inverted
index.

**DeepImpact** — Learned per-term impact retrieval combined with document
expansion.

**Dense retrieval** — Independently maps query/document to dense vectors and
ranks by vector similarity.

**Dense X Retrieval** — Work advocating proposition-level retrievable units,
not a generic synonym for dense search.

**Denoised negative** — Hard-negative candidate filtered/soft-labeled by a
stronger teacher to reduce false negatives.

**Direct preference optimization (DPO)** — Optimizes preference pairs relative
to a reference policy without an explicit learned reward model.

**DiskANN** — SSD-oriented graph ANN system using Vamana-style navigation and
compressed/in-memory routing structures.

**Distillation** — Training a smaller/retrieval model to match teacher scores,
distributions, attention, margins, or downstream utility.

**Document expansion** — Adds predicted query/term text to an index-only
document representation to improve lexical matching.

**DPR** — Dense Passage Retrieval; dual BERT encoders trained with positives,
in-batch negatives, and lexical hard negatives for open QA.

**Drift** — Change in query, corpus, model/index, or evaluation distribution that
can invalidate quality and calibration.

## E

**E5** — Embedding family trained from broad weakly supervised text pairs with
contrastive learning and query/passage prefixes.

**Embedding** — Numeric representation of query/document/unit; not anonymized
data and not evidence by itself.

**EMDR²** — End-to-end training approach for multi-document reader and
retriever using an EM-like objective.

**Entailment** — Whether evidence logically supports a claim. Semantic
similarity or answer mention does not imply entailment.

**Entity linking** — Mapping textual mentions to canonical entity IDs; a central
graph retrieval bottleneck.

**Evidence set** — One or more units collectively sufficient to support an
answer or required claims.

**Exact match (EM)** — Strict normalized string answer metric; insensitive to
many semantic and citation properties.

**Exact vector search** — Scores every vector, used as an oracle to measure ANN
loss on feasible corpora/samples.

## F

**Factual correctness** — Truth relative to reference/world/time, distinct from
faithfulness to provided context.

**FAISS** — Library/research system for efficient exact and approximate vector
search, including IVF/PQ and GPU algorithms.

**Faithfulness/groundedness** — Degree to which generated claims follow from
provided evidence; exact formulation varies across evaluators.

**False negative (retrieval training)** — Unlabeled document treated as negative
even though it is relevant/supporting.

**Federated retrieval** — Queries several independently owned or implemented
stores and fuses authorized results.

**FiD (Fusion-in-Decoder)** — Independently encodes question-passage pairs and
lets one decoder attend over their combined states.

**FiD-KD** — Distills reader signals from FiD into a retriever.

**Filter-aware ANN** — Integrates metadata/ACL predicates into vector search
rather than discarding unauthorized/nonmatching results only afterward.

**FLARE** — Active retrieval method that triggers retrieval from uncertain
tentative generation.

**FLOPS regularization** — Learned-sparse penalty based on average vocabulary
activation, intended to reduce inverted-index work.

**Freshness** — Operational property of capturing, indexing, selecting, and
answering from correct current/as-of evidence.

## G

**GaRAGe** — Human-annotated benchmark for relevance-aware factuality,
attribution, and insufficient-evidence deflection over web/private data.

**Generator** — Model producing answer text/structure from request and selected
evidence.

**Global search** — Corpus-level synthesis over themes/communities/summaries,
contrasted with local fact retrieval.

**GPL** — Domain adaptation using generated queries, mined negatives, and
cross-encoder teacher margins.

**GraphRAG** — Ambiguous family using graphs for construction, retrieval,
organization, or generation. Microsoft GraphRAG specifically uses extracted
graphs, communities, and reports for global/local search.

**GRIP** — 2026 framework representing retrieval and intermediate/answer/stop
states as structured tokens inside generation.

**GritLM** — Unified model trained for both embeddings and generation.

**Ground truth/qrels** — Human or constructed relevance/support labels used to
evaluate retrieval; often incomplete and version-specific.

## H

**Hallucination** — Generated content unsupported or false under a declared
definition. “Unsupported by context” and “factually false” are not identical.

**Hard negative** — Highly ranked or semantically similar nonpositive used for
training; may be an unlabeled false negative.

**Hierarchical RAG** — Retrieves across structural or generated summary levels,
including parent-child and RAPTOR-style trees.

**HiPRAG** — 2026 method adding hierarchical process rewards for search/no-search
decisions.

**HippoRAG** — Associative graph-memory approach using entities and Personalized
PageRank; HippoRAG 2 adds passage nodes/contextual relationships.

**HNSW** — Hierarchical navigable small-world graph ANN index with parameters
such as `M`, `efConstruction`, and `efSearch`.

**Hybrid retrieval** — Combines complementary retrieval families, commonly
sparse and dense.

**HyDE** — Generates a hypothetical answer document and embeds it for zero-shot
dense retrieval.

## I

**ICT (inverse cloze task)** — Self-supervised retriever pretraining using a
sentence as query and surrounding context as positive.

**IDF** — Inverse document frequency; higher weight for rarer terms under a
specific corpus/smoothing formula.

**Index generation** — Immutable, complete version of corpus representations
and index configuration served atomically.

**Index staleness** — Mismatch between source/current model and served index;
also used during retriever training when document embeddings lag parameters.

**Indirect prompt injection** — Malicious instructions embedded in retrieved
data/tool outputs that attempt to override policy or control actions.

**Information gain** — Reduction in uncertainty or uncovered needs after a
retrieval step; generally estimated by a proxy.

**Ingestion lag** — Time from upstream source change to availability in the
served index.

**INSTRUCTOR** — Instruction-conditioned embedding approach producing task-
specific representations from natural-language instructions.

**InfoNCE** — Contrastive softmax objective distinguishing a positive from
sampled negatives, controlled by temperature and negative distribution.

**Inverted index** — Maps terms to compressed postings of documents, positions,
frequencies, or learned impacts.

**IRCoT** — Interleaves chain-of-thought-style intermediate steps and retrieval
for multi-step questions.

**ITER-RETGEN** — Iterates answer generation and retrieval so generated output
guides later search.

**IVF (inverted file vector index)** — Partitions dense vectors by coarse
centroids and searches selected lists controlled by `nprobe`.

## J–K

**Joint training** — Optimizes retriever and generator/control together; creates
index refresh and evidence credit-assignment challenges.

**Judge** — Human, classifier, NLI model, or LLM evaluating quality. Judge error,
bias, prompt/version, and calibration must be audited.

**kNN-LM** — Interpolates a language model’s next-token distribution with tokens
from nearest hidden-state neighbors in an external datastore.

**Knowledge graph (KG)** — Typed entities and relations with provenance/time;
may be curated or extracted.

**KILT** — Shared Wikipedia snapshot benchmark combining downstream task
performance with provenance-aware retrieval metrics.

## L

**Late chunking** — Encodes longer document context before pooling token spans
into chunk vectors.

**Late interaction** — Independently precomputes token/patch vectors but performs
fine-grained query-unit interaction at scoring time.

**Latency percentiles** — p50/p95/p99 response times; tail latency is critical
for fan-out and sequential agent pipelines.

**LDAR** — 2026 distraction-aware retrieval learning focused on downstream
utility rather than monotonically increasing top-k.

**Learned sparse retrieval** — Neural term weighting/expansion retained as
sparse vocabulary vectors for inverted-index serving.

**Lineage** — Reverse/forward mapping among source versions, derived units,
embeddings, summaries, graph edges, outputs, caches, and training artifacts.

**Listwise ranking** — Learns or infers ordering using an entire candidate list,
not independent items or pairs.

**LLMLingua** — Prompt compression family using learned token selection/budget
control; LongLLMLingua is query-aware for long contexts.

**Long context** — Model input capacity large enough for full/large sources;
does not guarantee effective evidence use or remove retrieval economics.

**LongMemEval** — Benchmark for long-term conversational memory extraction,
updates, temporal reasoning, multi-session use, and abstention.

**Lost in the Middle** — Finding that long-context models often underuse evidence
at middle positions relative to beginning/end.

## M

**MAP (mean average precision)** — Mean of average precision over queries;
requires relevance judgments and accounts for ranks of multiple relevant items.

**Matryoshka representation** — Embedding trained so prefixes of several
dimensions remain useful, enabling adaptive storage/search/reranking.

**MaxSim** — Sum over query tokens of maximum similarity to any document token,
used by ColBERT/ColPali.

**Memory RAG** — Persistent external state with explicit write, retrieve,
consolidate, update, forget, delete, and use policies.

**Metadata filter** — Exact predicate over fields such as tenant, time, language,
jurisdiction, or product version.

**MIPS** — Maximum inner-product search, common for unnormalized dense scores.

**MMR (maximal marginal relevance)** — Greedy selection balancing query
relevance and redundancy against already selected evidence.

**MTEB/MMTEB** — Broad embedding evaluation suites; global averages do not
identify the best retriever for one RAG workload.

**Multi-hop retrieval** — Finds evidence connected across two or more reasoning
steps, often requiring decomposition, iteration, or graphs.

**Multi-vector retrieval** — Stores several token/region/patch vectors per unit
rather than one pooled embedding.

**MuSiQue** — Multi-hop QA benchmark designed to reduce shortcut reasoning.

## N–O

**nDCG@k** — Discounted cumulative gain normalized by ideal ranking, supporting
graded relevance.

**Negative mining** — Selecting nonpositive training documents, including
random, in-batch, lexical, ANN, teacher-denoised, or adversarial negatives.

**NoMIRACL** — Multilingual benchmark reporting hallucination when evidence is
absent and miss when evidence exists; it does not measure full answer accuracy.

**Nougat** — Vision-to-markup model for parsing scientific document pages.

**Oracle context** — Gold supporting evidence given directly to the generator,
used to isolate generator capability.

**Oracle candidate recall** — Maximum possible retrieval/selection performance
given the first-stage candidate pool.

**ORQA** — Latent dense retrieval trained from answer strings, initialized with
ICT, preceding REALM/DPR.

**Outcome reward** — RL reward based on final answer; vulnerable to spurious
search/evidence credit.

**Over-search/under-search** — Retrieving unnecessarily versus failing to
retrieve when needed; report both.

## P

**Parent-child retrieval** — Indexes small child units for matching and expands
to a larger parent for coherent generation.

**Passage** — Contiguous text retrieval unit; length/boundary definition must be
specified.

**Personalized PageRank** — Random-walk ranking biased to query/seed nodes, used
for associative graph retrieval.

**PoisonedRAG** — Targeted knowledge-poisoning attack that crafts retrieval and
generation payloads; not a defensive method.

**Precision@k** — Relevant retrieved items divided by k (or returned count under
a declared convention).

**Process reward** — RL signal for intermediate retrieval/search behavior rather
than only final answer.

**Prompt RAG** — Serializes retrieved evidence into the generator input without
architectural integration.

**Proposition retrieval** — Indexes atomic self-contained claims with parent/
source alignment.

**Provenance** — Authentic source identity, version, location, transformation,
and custody for evidence.

**Pseudo-relevance feedback (PRF)** — Assumes top initial results are relevant
and derives expansion/reweighting; can cause query drift.

**PQ/OPQ** — Product quantization encodes vector subvectors with codebooks;
optimized PQ learns a rotation to reduce distortion.

## Q

**Q-RAG** — 2026 value-based RL system that learns evidence selection/STOP with
a frozen LLM.

**QAMPARI** — Long-form/list QA benchmark often used for attributed generation.

**Query2Doc** — LLM query expansion that appends a generated pseudo-document
while retaining original query terms.

**Query drift** — Transformation/feedback moves search away from the user’s
actual information need.

**Query likelihood** — Ranks documents by smoothed probability of generating
the query terms.

**Query routing** — Chooses retriever/source/tool/no-search/long-context path.

**Qrels** — Query-document relevance judgments; incomplete qrels bias evaluation
and hard-negative training.

## R

**RAG-Sequence** — Original RAG variant marginalizing one latent document for
the entire generated sequence.

**RAG-Token** — Original RAG variant marginalizing latent documents separately
at each output token.

**RAGAS** — Automated reference-light evaluator framework; metrics/judges/APIs
must be versioned and calibrated.

**RAGChecker** — Claim-oriented framework diagnosing retriever and generator
contributions/failures.

**RAGTruth** — Human span-level hallucination corpus for generated outputs under
provided references; not a retrieval benchmark.

**RankRAG** — Instruction-tunes one LLM for candidate ranking and answer
generation; first-stage recall remains a ceiling.

**RankGPT** — LLM listwise/permutation reranking, often applied with sliding
windows.

**RAPTOR** — Recursive clustering and abstractive summarization into a retrieval
tree.

**RaLMSpec** — Speculative retrieval/batched verification for faster iterative
retrieval-augmented LM serving.

**Recall@k** — Fraction of relevant items found in top k; definition may require
one item, all supporting facts, or complete alternative provenance sets.

**RECOMP** — Trained extractive/abstractive evidence compression with selective
empty augmentation.

**Relevance** — Query-document usefulness under a judgment guideline; topical
similarity, support, and authority are different.

**RemoteRAG/PRAG** — Research directions for privacy-preserving remote/
distributed similarity retrieval under explicit assumptions.

**Reranker regret** — Relevant evidence present in candidates but pushed below
the retained cutoff by reranking.

**RETRO** — Retrieval-enhanced language model using chunk-neighbor cross-attention
during pretraining/inference over a massive datastore.

**Retriever** — Component returning ranked external units for a query/state.

**RRF (reciprocal-rank fusion)** — Sums inverse shifted ranks across runs; robust
to incomparable raw score scales.

**Risk-coverage curve** — Error/risk among answered examples as answer coverage
varies by confidence threshold.

**RM3** — Relevance-model pseudo-feedback interpolation widely used in lexical
retrieval.

## S

**SafeRAG** — Security benchmark covering noise, conflicts, advertisements, and
denial-of-service attacks; not a universal safety guarantee.

**ScaNN** — ANN system combining partitioning, anisotropic quantization, and
reordering for inner-product search.

**Score calibration** — Transforms retrieval/ranking scores into comparable
probabilities or stable fusion inputs using labeled data.

**Self-RAG** — Trains reflection tokens for retrieve/relevance/support/utility
control during generation.

**Semantic cache** — Reuses results/answers for similar queries; high privacy,
intent, time, and invalidation risk.

**Selector** — Chooses the evidence set under relevance, coverage, redundancy,
authority, freshness, risk, and token constraints.

**Set selection** — Optimizes collective evidence coverage rather than
independent top-k ranking.

**SFT (supervised fine-tuning)** — Likelihood training on demonstrations such as
answers, citations, or search trajectories.

**SPLADE** — Learned sparse vocabulary expansion/weighting with ranking and
sparsity objectives.

**SPANN** — Hybrid memory/disk ANN using in-memory centroids and disk posting
lists with closure augmentation/query pruning.

**Sparse retrieval** — Scores nonzero vocabulary/feature overlap, usually via an
inverted index; includes lexical and learned sparse methods.

**Source diversity** — Independence/coverage across source families; duplicates
do not count as corroboration.

**Source of truth** — Authoritative upstream state used for reconciliation,
permissions, corrections, and deletion.

**Stale answer rate** — Fraction of answers using superseded/temporally
incompatible evidence under the declared task.

**Sufficient context** — Evidence contains enough information to answer, beyond
individual passage relevance.

## T

**Table RAG** — Retrieval and reasoning over structured tables, often combining
schema/table/row/cell retrieval with SQL/calculation.

**Temperature** — Contrastive softmax/decode parameter controlling distribution
sharpness; distinct uses should not be conflated.

**Temporal RAG** — Uses valid/event/system time, versions, and temporal
constraints to answer current or historical questions.

**Tombstone** — Deletion marker preventing stale events/replicas from
resurrecting removed content.

**Top-k truncation** — Only highest retrieved candidates enter a latent sum or
context, imposing a gradient and recall ceiling.

**TREC RAG** — Annual NIST shared evaluation for retrieval, generation, nuggets,
and sentence-level citations; pin year and judgments.

**Trust domain** — Set of data/components/principals sharing security assumptions
and policy boundary.

**TTFT (time to first token)** — Delay before streaming output begins; includes
retrieval and model prefill in end-to-end measurement.

## U–V

**Unanswerable query** — No sufficient authorized evidence exists; distinct from
technical retrieval failure.

**Utility-aware retrieval** — Optimizes downstream answer/claim performance
rather than relevance alone.

**Valid time** — Interval when a fact is true in the represented world.

**Vamana** — Pruned navigable graph underlying DiskANN-style search.

**Vector database** — Storage/search system for embeddings plus metadata; does
not by itself define chunking, retriever quality, generation, or evaluation.

**Verifier** — Component checking claims, citations, schema, calculations,
policy, or safety. It is fallible and requires calibration/audit.

**Visual RAG** — Retrieves and generates from page/image/region/patch evidence,
not merely OCR text.

**VisRAG** — Direct visual document retrieval plus VLM generation approach.

## W–Z

**WAND** — Dynamic-pruning top-k algorithm using term score upper bounds.

**Weak supervision** — Labels derived from answer strings, citations, clicks,
teachers, or synthetic generation rather than direct expert relevance.

**Working memory** — Temporary state for the current task, distinct from
persistent user/agent memory.

**xRAG/latent compression** — Compresses retrieved text into learned embedding
tokens for a generator, improving token efficiency at an auditability cost.

**Zero-shot retrieval** — Evaluation on tasks/domains without task-specific
relevance fine-tuning; contamination and broad pretraining still matter.