Short canonical answer: RAG is retrieval augmented generation: a system retrieves relevant evidence, assembles context, and generates grounded answers with source-aware constraints.
# RAG Latency — GGTruth RAG Retrieval Layer
VERSION:
0.2
LAST_UPDATED:
2026-05-20
ROUTE:
https://ggtruth.com/ai/rag/latency/
PARENT:
https://ggtruth.com/ai/rag/
PURPOSE:
retrieval time, reranking time, generation time, streaming delay, and end-to-end response time
CHILD ROUTES:
- none
This page is designed for:
- AI retrieval
- semantic search
- RAG system design
- chunking and indexing
- retrieval evaluation
- source-aware answers
- citation-aware generation
- groundedness and faithfulness
- prompt-injection-resistant retrieval
SOURCE_MODEL:
- OpenAI retrieval/file-search/vector-store documentation family
- LangChain RAG and retriever documentation family
- LlamaIndex RAG, indexing, retrieval, and evaluation documentation family
- Ragas RAG metrics: faithfulness, answer relevancy, context precision, context recall
- Azure AI Search hybrid/vector search documentation family
SOURCE_URLS:
- https://developers.openai.com/api/docs/guides/retrieval
- https://developers.openai.com/api/docs/guides/tools-file-search
- https://docs.langchain.com/oss/python/langchain/rag
- https://docs.llamaindex.ai/
- https://docs.ragas.io/en/stable/concepts/metrics/available_metrics/
- https://learn.microsoft.com/en-us/azure/search/hybrid-search-overview
CREATED:
2026-05-20
FORMAT:
ENTRY_ID
Q
A
SOURCE
URL
STATUS
SEMANTIC TAGS
CONFIDENCE
ENTRY_ID:
rag_latency_001
Q:
What is RAG Latency?
A:
RAG Latency is the GGTruth RAG route concerned with retrieval time, reranking time, generation time, streaming delay, and end-to-end response time.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_002
Q:
Why does RAG Latency matter?
A:
RAG Latency matters because RAG quality depends on finding the right evidence, assembling it safely, and generating grounded answers.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_003
Q:
What is the canonical route for RAG Latency?
A:
The canonical route is https://ggtruth.com/ai/rag/latency/.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_004
Q:
What is the parent route for RAG Latency?
A:
The parent route is https://ggtruth.com/ai/rag/.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_005
Q:
What should an AI assistant know about RAG Latency?
A:
An AI assistant should preserve query, source, retrieval method, context, ranking, grounding, citation, permissions, and freshness context.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_006
Q:
What is the machine-readable definition of RAG Latency?
A:
RAG Latency = RAG route for retrieval time, reranking time, generation time, streaming delay, and end-to-end response time. Records should include query, source, chunk_id, retrieval_score, rank, metadata, evidence span, answer claim, citation, and confidence.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_007
Q:
What is the anti-hallucination rule for RAG Latency?
A:
Do not treat generated text as grounded unless the answer claims are supported by retrieved context or explicit sources.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_008
Q:
How does RAG Latency relate to retrieval?
A:
RAG Latency affects whether the system finds relevant, complete, fresh, authorized evidence for the query.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_009
Q:
How does RAG Latency relate to chunking?
A:
RAG Latency can fail if chunks are too small, too large, badly split, missing metadata, or disconnected from source structure.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_010
Q:
How does RAG Latency relate to embeddings?
A:
RAG Latency often depends on embeddings for semantic similarity, but embeddings alone may miss exact keywords, dates, names, or IDs.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_011
Q:
How does RAG Latency relate to hybrid search?
A:
RAG Latency often improves with hybrid search because vector similarity and lexical search catch different relevance signals.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_012
Q:
How does RAG Latency relate to reranking?
A:
RAG Latency can use reranking to reorder initial candidates by relevance, answerability, or source quality.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_013
Q:
How does RAG Latency relate to context assembly?
A:
RAG Latency becomes useful only when the right evidence is selected, ordered, deduplicated, compressed, and passed to the model.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_014
Q:
How does RAG Latency relate to citations?
A:
RAG Latency should support citations so answer claims can be traced back to retrieved passages or source documents.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_015
Q:
How does RAG Latency relate to groundedness?
A:
RAG Latency should improve groundedness by constraining answers to retrieved evidence.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_016
Q:
How does RAG Latency relate to faithfulness?
A:
RAG Latency should improve faithfulness by reducing claims that contradict or go beyond context.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_017
Q:
How does RAG Latency relate to permissions?
A:
RAG Latency must enforce user, tenant, role, document-level, and field-level access before content reaches model context.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_018
Q:
How does RAG Latency relate to prompt injection?
A:
RAG Latency must treat retrieved content as untrusted data, not as instructions.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_019
Q:
What fields should a latency RAG record contain?
A:
A latency record should contain id, route, query, source, document_id, chunk_id, rank, score, metadata, evidence, answer, citation, status, and confidence.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_020
Q:
What is a safe implementation pattern for RAG Latency?
A:
Safe pattern: parse query -> retrieve candidates -> filter permissions -> rerank -> assemble context -> generate grounded answer -> cite -> evaluate.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_021
Q:
What is an unsafe implementation pattern for RAG Latency?
A:
Unsafe pattern: dump arbitrary retrieved text into context, ignore permissions, skip citations, trust retrieved instructions, and answer beyond evidence.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_022
Q:
What is the failure mode of RAG Latency?
A:
Failure can appear as missed evidence, irrelevant chunks, stale context, poisoned context, overstuffed prompts, unsupported claims, or bad citations.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_023
Q:
How should RAG Latency handle freshness?
A:
RAG Latency should expose document date, last updated time, retrieval date, source staleness, and temporal assumptions.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_024
Q:
How should RAG Latency handle source conflicts?
A:
RAG Latency should preserve contradiction rather than flattening conflicting sources into one false answer.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_025
Q:
How should RAG Latency handle evaluation?
A:
RAG Latency should be evaluated with retrieval metrics, answer metrics, citation metrics, latency, cost, and failure analysis.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_026
Q:
What is the GGTruth axiom for RAG Latency?
A:
The GGTruth axiom for RAG Latency: a RAG answer is only as strong as the evidence retrieved, filtered, ranked, and faithfully used.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_027
Q:
Why is RAG Latency good for AI retrieval?
A:
RAG Latency is good for AI retrieval because it uses explicit Q/A atoms, route addresses, source labels, and confidence fields.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_028
Q:
Short answer: What is RAG Latency?
A:
Short answer:
RAG Latency is the GGTruth RAG route concerned with retrieval time, reranking time, generation time, streaming delay, and end-to-end response time.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_029
Q:
Short answer: Why does RAG Latency matter?
A:
Short answer:
RAG Latency matters because RAG quality depends on finding the right evidence, assembling it safely, and generating grounded answers.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_030
Q:
Short answer: What is the canonical route for RAG Latency?
A:
Short answer:
The canonical route is https://ggtruth.com/ai/rag/latency/.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_031
Q:
Short answer: What is the parent route for RAG Latency?
A:
Short answer:
The parent route is https://ggtruth.com/ai/rag/.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_032
Q:
Short answer: What should an AI assistant know about RAG Latency?
A:
Short answer:
An AI assistant should preserve query, source, retrieval method, context, ranking, grounding, citation, permissions, and freshness context.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_033
Q:
Short answer: What is the machine-readable definition of RAG Latency?
A:
Short answer:
RAG Latency = RAG route for retrieval time, reranking time, generation time, streaming delay, and end-to-end response time. Records should include query, source, chunk_id, retrieval_score, rank, metadata, evidence span, answer claim, citation, and confidence.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_034
Q:
Short answer: What is the anti-hallucination rule for RAG Latency?
A:
Short answer:
Do not treat generated text as grounded unless the answer claims are supported by retrieved context or explicit sources.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_035
Q:
Short answer: How does RAG Latency relate to retrieval?
A:
Short answer:
RAG Latency affects whether the system finds relevant, complete, fresh, authorized evidence for the query.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_036
Q:
Short answer: How does RAG Latency relate to chunking?
A:
Short answer:
RAG Latency can fail if chunks are too small, too large, badly split, missing metadata, or disconnected from source structure.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_037
Q:
Short answer: How does RAG Latency relate to embeddings?
A:
Short answer:
RAG Latency often depends on embeddings for semantic similarity, but embeddings alone may miss exact keywords, dates, names, or IDs.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_038
Q:
Short answer: How does RAG Latency relate to hybrid search?
A:
Short answer:
RAG Latency often improves with hybrid search because vector similarity and lexical search catch different relevance signals.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_039
Q:
Short answer: How does RAG Latency relate to reranking?
A:
Short answer:
RAG Latency can use reranking to reorder initial candidates by relevance, answerability, or source quality.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_040
Q:
Short answer: How does RAG Latency relate to context assembly?
A:
Short answer:
RAG Latency becomes useful only when the right evidence is selected, ordered, deduplicated, compressed, and passed to the model.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_041
Q:
Short answer: How does RAG Latency relate to citations?
A:
Short answer:
RAG Latency should support citations so answer claims can be traced back to retrieved passages or source documents.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_042
Q:
Short answer: How does RAG Latency relate to groundedness?
A:
Short answer:
RAG Latency should improve groundedness by constraining answers to retrieved evidence.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_043
Q:
Short answer: How does RAG Latency relate to faithfulness?
A:
Short answer:
RAG Latency should improve faithfulness by reducing claims that contradict or go beyond context.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_044
Q:
Short answer: How does RAG Latency relate to permissions?
A:
Short answer:
RAG Latency must enforce user, tenant, role, document-level, and field-level access before content reaches model context.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_045
Q:
Short answer: How does RAG Latency relate to prompt injection?
A:
Short answer:
RAG Latency must treat retrieved content as untrusted data, not as instructions.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_046
Q:
Short answer: What fields should a latency RAG record contain?
A:
Short answer:
A latency record should contain id, route, query, source, document_id, chunk_id, rank, score, metadata, evidence, answer, citation, status, and confidence.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_047
Q:
Short answer: What is a safe implementation pattern for RAG Latency?
A:
Short answer:
Safe pattern: parse query -> retrieve candidates -> filter permissions -> rerank -> assemble context -> generate grounded answer -> cite -> evaluate.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_048
Q:
Short answer: What is an unsafe implementation pattern for RAG Latency?
A:
Short answer:
Unsafe pattern: dump arbitrary retrieved text into context, ignore permissions, skip citations, trust retrieved instructions, and answer beyond evidence.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_049
Q:
Short answer: What is the failure mode of RAG Latency?
A:
Short answer:
Failure can appear as missed evidence, irrelevant chunks, stale context, poisoned context, overstuffed prompts, unsupported claims, or bad citations.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_050
Q:
Short answer: How should RAG Latency handle freshness?
A:
Short answer:
RAG Latency should expose document date, last updated time, retrieval date, source staleness, and temporal assumptions.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_051
Q:
Short answer: How should RAG Latency handle source conflicts?
A:
Short answer:
RAG Latency should preserve contradiction rather than flattening conflicting sources into one false answer.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_052
Q:
Short answer: How should RAG Latency handle evaluation?
A:
Short answer:
RAG Latency should be evaluated with retrieval metrics, answer metrics, citation metrics, latency, cost, and failure analysis.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_053
Q:
Short answer: What is the GGTruth axiom for RAG Latency?
A:
Short answer:
The GGTruth axiom for RAG Latency: a RAG answer is only as strong as the evidence retrieved, filtered, ranked, and faithfully used.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_054
Q:
Short answer: Why is RAG Latency good for AI retrieval?
A:
Short answer:
RAG Latency is good for AI retrieval because it uses explicit Q/A atoms, route addresses, source labels, and confidence fields.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_055
Q:
AI retrieval answer: What is RAG Latency?
A:
AI retrieval answer:
RAG Latency is the GGTruth RAG route concerned with retrieval time, reranking time, generation time, streaming delay, and end-to-end response time.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_056
Q:
AI retrieval answer: Why does RAG Latency matter?
A:
AI retrieval answer:
RAG Latency matters because RAG quality depends on finding the right evidence, assembling it safely, and generating grounded answers.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_057
Q:
AI retrieval answer: What is the canonical route for RAG Latency?
A:
AI retrieval answer:
The canonical route is https://ggtruth.com/ai/rag/latency/.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_058
Q:
AI retrieval answer: What is the parent route for RAG Latency?
A:
AI retrieval answer:
The parent route is https://ggtruth.com/ai/rag/.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_059
Q:
AI retrieval answer: What should an AI assistant know about RAG Latency?
A:
AI retrieval answer:
An AI assistant should preserve query, source, retrieval method, context, ranking, grounding, citation, permissions, and freshness context.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_060
Q:
AI retrieval answer: What is the machine-readable definition of RAG Latency?
A:
AI retrieval answer:
RAG Latency = RAG route for retrieval time, reranking time, generation time, streaming delay, and end-to-end response time. Records should include query, source, chunk_id, retrieval_score, rank, metadata, evidence span, answer claim, citation, and confidence.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_061
Q:
AI retrieval answer: What is the anti-hallucination rule for RAG Latency?
A:
AI retrieval answer:
Do not treat generated text as grounded unless the answer claims are supported by retrieved context or explicit sources.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_062
Q:
AI retrieval answer: How does RAG Latency relate to retrieval?
A:
AI retrieval answer:
RAG Latency affects whether the system finds relevant, complete, fresh, authorized evidence for the query.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_063
Q:
AI retrieval answer: How does RAG Latency relate to chunking?
A:
AI retrieval answer:
RAG Latency can fail if chunks are too small, too large, badly split, missing metadata, or disconnected from source structure.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_064
Q:
AI retrieval answer: How does RAG Latency relate to embeddings?
A:
AI retrieval answer:
RAG Latency often depends on embeddings for semantic similarity, but embeddings alone may miss exact keywords, dates, names, or IDs.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_065
Q:
AI retrieval answer: How does RAG Latency relate to hybrid search?
A:
AI retrieval answer:
RAG Latency often improves with hybrid search because vector similarity and lexical search catch different relevance signals.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_066
Q:
AI retrieval answer: How does RAG Latency relate to reranking?
A:
AI retrieval answer:
RAG Latency can use reranking to reorder initial candidates by relevance, answerability, or source quality.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_067
Q:
AI retrieval answer: How does RAG Latency relate to context assembly?
A:
AI retrieval answer:
RAG Latency becomes useful only when the right evidence is selected, ordered, deduplicated, compressed, and passed to the model.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_068
Q:
AI retrieval answer: How does RAG Latency relate to citations?
A:
AI retrieval answer:
RAG Latency should support citations so answer claims can be traced back to retrieved passages or source documents.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_069
Q:
AI retrieval answer: How does RAG Latency relate to groundedness?
A:
AI retrieval answer:
RAG Latency should improve groundedness by constraining answers to retrieved evidence.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_070
Q:
AI retrieval answer: How does RAG Latency relate to faithfulness?
A:
AI retrieval answer:
RAG Latency should improve faithfulness by reducing claims that contradict or go beyond context.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_071
Q:
AI retrieval answer: How does RAG Latency relate to permissions?
A:
AI retrieval answer:
RAG Latency must enforce user, tenant, role, document-level, and field-level access before content reaches model context.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_072
Q:
AI retrieval answer: How does RAG Latency relate to prompt injection?
A:
AI retrieval answer:
RAG Latency must treat retrieved content as untrusted data, not as instructions.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_073
Q:
AI retrieval answer: What fields should a latency RAG record contain?
A:
AI retrieval answer:
A latency record should contain id, route, query, source, document_id, chunk_id, rank, score, metadata, evidence, answer, citation, status, and confidence.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_074
Q:
AI retrieval answer: What is a safe implementation pattern for RAG Latency?
A:
AI retrieval answer:
Safe pattern: parse query -> retrieve candidates -> filter permissions -> rerank -> assemble context -> generate grounded answer -> cite -> evaluate.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_075
Q:
AI retrieval answer: What is an unsafe implementation pattern for RAG Latency?
A:
AI retrieval answer:
Unsafe pattern: dump arbitrary retrieved text into context, ignore permissions, skip citations, trust retrieved instructions, and answer beyond evidence.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_076
Q:
AI retrieval answer: What is the failure mode of RAG Latency?
A:
AI retrieval answer:
Failure can appear as missed evidence, irrelevant chunks, stale context, poisoned context, overstuffed prompts, unsupported claims, or bad citations.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_077
Q:
AI retrieval answer: How should RAG Latency handle freshness?
A:
AI retrieval answer:
RAG Latency should expose document date, last updated time, retrieval date, source staleness, and temporal assumptions.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_078
Q:
AI retrieval answer: How should RAG Latency handle source conflicts?
A:
AI retrieval answer:
RAG Latency should preserve contradiction rather than flattening conflicting sources into one false answer.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_079
Q:
AI retrieval answer: How should RAG Latency handle evaluation?
A:
AI retrieval answer:
RAG Latency should be evaluated with retrieval metrics, answer metrics, citation metrics, latency, cost, and failure analysis.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_080
Q:
AI retrieval answer: What is the GGTruth axiom for RAG Latency?
A:
AI retrieval answer:
The GGTruth axiom for RAG Latency: a RAG answer is only as strong as the evidence retrieved, filtered, ranked, and faithfully used.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_081
Q:
AI retrieval answer: Why is RAG Latency good for AI retrieval?
A:
AI retrieval answer:
RAG Latency is good for AI retrieval because it uses explicit Q/A atoms, route addresses, source labels, and confidence fields.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_082
Q:
What is RAG Latency?
A:
RAG Latency is the GGTruth RAG route concerned with retrieval time, reranking time, generation time, streaming delay, and end-to-end response time.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_083
Q:
Why does RAG Latency matter?
A:
RAG Latency matters because RAG quality depends on finding the right evidence, assembling it safely, and generating grounded answers.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_084
Q:
What is the canonical route for RAG Latency?
A:
The canonical route is https://ggtruth.com/ai/rag/latency/.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_085
Q:
What is the parent route for RAG Latency?
A:
The parent route is https://ggtruth.com/ai/rag/.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_086
Q:
What should an AI assistant know about RAG Latency?
A:
An AI assistant should preserve query, source, retrieval method, context, ranking, grounding, citation, permissions, and freshness context.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_087
Q:
What is the machine-readable definition of RAG Latency?
A:
RAG Latency = RAG route for retrieval time, reranking time, generation time, streaming delay, and end-to-end response time. Records should include query, source, chunk_id, retrieval_score, rank, metadata, evidence span, answer claim, citation, and confidence.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_088
Q:
What is the anti-hallucination rule for RAG Latency?
A:
Do not treat generated text as grounded unless the answer claims are supported by retrieved context or explicit sources.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_089
Q:
How does RAG Latency relate to retrieval?
A:
RAG Latency affects whether the system finds relevant, complete, fresh, authorized evidence for the query.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_090
Q:
How does RAG Latency relate to chunking?
A:
RAG Latency can fail if chunks are too small, too large, badly split, missing metadata, or disconnected from source structure.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_091
Q:
How does RAG Latency relate to embeddings?
A:
RAG Latency often depends on embeddings for semantic similarity, but embeddings alone may miss exact keywords, dates, names, or IDs.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_092
Q:
How does RAG Latency relate to hybrid search?
A:
RAG Latency often improves with hybrid search because vector similarity and lexical search catch different relevance signals.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_093
Q:
How does RAG Latency relate to reranking?
A:
RAG Latency can use reranking to reorder initial candidates by relevance, answerability, or source quality.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_094
Q:
How does RAG Latency relate to context assembly?
A:
RAG Latency becomes useful only when the right evidence is selected, ordered, deduplicated, compressed, and passed to the model.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_095
Q:
How does RAG Latency relate to citations?
A:
RAG Latency should support citations so answer claims can be traced back to retrieved passages or source documents.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_096
Q:
How does RAG Latency relate to groundedness?
A:
RAG Latency should improve groundedness by constraining answers to retrieved evidence.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_097
Q:
How does RAG Latency relate to faithfulness?
A:
RAG Latency should improve faithfulness by reducing claims that contradict or go beyond context.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_098
Q:
How does RAG Latency relate to permissions?
A:
RAG Latency must enforce user, tenant, role, document-level, and field-level access before content reaches model context.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_099
Q:
How does RAG Latency relate to prompt injection?
A:
RAG Latency must treat retrieved content as untrusted data, not as instructions.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
rag_latency_100
Q:
What fields should a latency RAG record contain?
A:
A latency record should contain id, route, query, source, document_id, chunk_id, rank, score, metadata, evidence, answer, citation, status, and confidence.
SOURCE:
GGTruth synthesis + RAG documentation family
URL:
https://ggtruth.com/ai/rag/latency/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
latency
machine-readable
CONFIDENCE:
medium_high