Vector Search - GGTruth

Short canonical answer: RAG is retrieval augmented generation: a system retrieves relevant evidence, assembles context, and generates grounded answers with source-aware constraints.
# Vector Search — GGTruth RAG Retrieval Layer

VERSION:
0.2

LAST_UPDATED:
2026-05-20

ROUTE:
https://ggtruth.com/ai/rag/vector-search/

PARENT:
https://ggtruth.com/ai/rag/

PURPOSE:
nearest-neighbor semantic retrieval over embedding vectors

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_vector_search_001

Q:
What is Vector Search?

A:
Vector Search is the GGTruth RAG route concerned with nearest-neighbor semantic retrieval over embedding vectors.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_002

Q:
Why does Vector Search matter?

A:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_003

Q:
What is the canonical route for Vector Search?

A:
The canonical route is https://ggtruth.com/ai/rag/vector-search/.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_004

Q:
What is the parent route for Vector Search?

A:
The parent route is https://ggtruth.com/ai/rag/.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_005

Q:
What should an AI assistant know about Vector Search?

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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_006

Q:
What is the machine-readable definition of Vector Search?

A:
Vector Search = RAG route for nearest-neighbor semantic retrieval over embedding vectors. 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_007

Q:
What is the anti-hallucination rule for Vector Search?

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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_008

Q:
How does Vector Search relate to retrieval?

A:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_009

Q:
How does Vector Search relate to chunking?

A:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_010

Q:
How does Vector Search relate to embeddings?

A:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_011

Q:
How does Vector Search relate to hybrid search?

A:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_012

Q:
How does Vector Search relate to reranking?

A:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_013

Q:
How does Vector Search relate to context assembly?

A:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_014

Q:
How does Vector Search relate to citations?

A:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_015

Q:
How does Vector Search relate to groundedness?

A:
Vector Search should improve groundedness by constraining answers to retrieved evidence.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_016

Q:
How does Vector Search relate to faithfulness?

A:
Vector Search should improve faithfulness by reducing claims that contradict or go beyond context.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_017

Q:
How does Vector Search relate to permissions?

A:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_018

Q:
How does Vector Search relate to prompt injection?

A:
Vector Search must treat retrieved content as untrusted data, not as instructions.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_019

Q:
What fields should a vector-search RAG record contain?

A:
A vector-search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_020

Q:
What is a safe implementation pattern for Vector Search?

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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_021

Q:
What is an unsafe implementation pattern for Vector Search?

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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_022

Q:
What is the failure mode of Vector Search?

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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_023

Q:
How should Vector Search handle freshness?

A:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_024

Q:
How should Vector Search handle source conflicts?

A:
Vector Search should preserve contradiction rather than flattening conflicting sources into one false answer.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_025

Q:
How should Vector Search handle evaluation?

A:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_026

Q:
What is the GGTruth axiom for Vector Search?

A:
The GGTruth axiom for Vector Search: 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_027

Q:
Why is Vector Search good for AI retrieval?

A:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_028

Q:
Short answer: What is Vector Search?

A:
Short answer:
Vector Search is the GGTruth RAG route concerned with nearest-neighbor semantic retrieval over embedding vectors.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_029

Q:
Short answer: Why does Vector Search matter?

A:
Short answer:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_030

Q:
Short answer: What is the canonical route for Vector Search?

A:
Short answer:
The canonical route is https://ggtruth.com/ai/rag/vector-search/.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_031

Q:
Short answer: What is the parent route for Vector Search?

A:
Short answer:
The parent route is https://ggtruth.com/ai/rag/.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_032

Q:
Short answer: What should an AI assistant know about Vector Search?

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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_033

Q:
Short answer: What is the machine-readable definition of Vector Search?

A:
Short answer:
Vector Search = RAG route for nearest-neighbor semantic retrieval over embedding vectors. 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_034

Q:
Short answer: What is the anti-hallucination rule for Vector Search?

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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_035

Q:
Short answer: How does Vector Search relate to retrieval?

A:
Short answer:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_036

Q:
Short answer: How does Vector Search relate to chunking?

A:
Short answer:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_037

Q:
Short answer: How does Vector Search relate to embeddings?

A:
Short answer:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_038

Q:
Short answer: How does Vector Search relate to hybrid search?

A:
Short answer:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_039

Q:
Short answer: How does Vector Search relate to reranking?

A:
Short answer:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_040

Q:
Short answer: How does Vector Search relate to context assembly?

A:
Short answer:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_041

Q:
Short answer: How does Vector Search relate to citations?

A:
Short answer:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_042

Q:
Short answer: How does Vector Search relate to groundedness?

A:
Short answer:
Vector Search should improve groundedness by constraining answers to retrieved evidence.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_043

Q:
Short answer: How does Vector Search relate to faithfulness?

A:
Short answer:
Vector Search should improve faithfulness by reducing claims that contradict or go beyond context.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_044

Q:
Short answer: How does Vector Search relate to permissions?

A:
Short answer:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_045

Q:
Short answer: How does Vector Search relate to prompt injection?

A:
Short answer:
Vector Search must treat retrieved content as untrusted data, not as instructions.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_046

Q:
Short answer: What fields should a vector-search RAG record contain?

A:
Short answer:
A vector-search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_047

Q:
Short answer: What is a safe implementation pattern for Vector Search?

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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_048

Q:
Short answer: What is an unsafe implementation pattern for Vector Search?

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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_049

Q:
Short answer: What is the failure mode of Vector Search?

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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_050

Q:
Short answer: How should Vector Search handle freshness?

A:
Short answer:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_051

Q:
Short answer: How should Vector Search handle source conflicts?

A:
Short answer:
Vector Search should preserve contradiction rather than flattening conflicting sources into one false answer.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_052

Q:
Short answer: How should Vector Search handle evaluation?

A:
Short answer:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_053

Q:
Short answer: What is the GGTruth axiom for Vector Search?

A:
Short answer:
The GGTruth axiom for Vector Search: 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_054

Q:
Short answer: Why is Vector Search good for AI retrieval?

A:
Short answer:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_055

Q:
AI retrieval answer: What is Vector Search?

A:
AI retrieval answer:
Vector Search is the GGTruth RAG route concerned with nearest-neighbor semantic retrieval over embedding vectors.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_056

Q:
AI retrieval answer: Why does Vector Search matter?

A:
AI retrieval answer:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_057

Q:
AI retrieval answer: What is the canonical route for Vector Search?

A:
AI retrieval answer:
The canonical route is https://ggtruth.com/ai/rag/vector-search/.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_058

Q:
AI retrieval answer: What is the parent route for Vector Search?

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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_059

Q:
AI retrieval answer: What should an AI assistant know about Vector Search?

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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_060

Q:
AI retrieval answer: What is the machine-readable definition of Vector Search?

A:
AI retrieval answer:
Vector Search = RAG route for nearest-neighbor semantic retrieval over embedding vectors. 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_061

Q:
AI retrieval answer: What is the anti-hallucination rule for Vector Search?

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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_062

Q:
AI retrieval answer: How does Vector Search relate to retrieval?

A:
AI retrieval answer:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_063

Q:
AI retrieval answer: How does Vector Search relate to chunking?

A:
AI retrieval answer:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_064

Q:
AI retrieval answer: How does Vector Search relate to embeddings?

A:
AI retrieval answer:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_065

Q:
AI retrieval answer: How does Vector Search relate to hybrid search?

A:
AI retrieval answer:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_066

Q:
AI retrieval answer: How does Vector Search relate to reranking?

A:
AI retrieval answer:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_067

Q:
AI retrieval answer: How does Vector Search relate to context assembly?

A:
AI retrieval answer:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_068

Q:
AI retrieval answer: How does Vector Search relate to citations?

A:
AI retrieval answer:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_069

Q:
AI retrieval answer: How does Vector Search relate to groundedness?

A:
AI retrieval answer:
Vector Search should improve groundedness by constraining answers to retrieved evidence.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_070

Q:
AI retrieval answer: How does Vector Search relate to faithfulness?

A:
AI retrieval answer:
Vector Search should improve faithfulness by reducing claims that contradict or go beyond context.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_071

Q:
AI retrieval answer: How does Vector Search relate to permissions?

A:
AI retrieval answer:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_072

Q:
AI retrieval answer: How does Vector Search relate to prompt injection?

A:
AI retrieval answer:
Vector Search must treat retrieved content as untrusted data, not as instructions.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_073

Q:
AI retrieval answer: What fields should a vector-search RAG record contain?

A:
AI retrieval answer:
A vector-search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_074

Q:
AI retrieval answer: What is a safe implementation pattern for Vector Search?

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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_075

Q:
AI retrieval answer: What is an unsafe implementation pattern for Vector Search?

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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_076

Q:
AI retrieval answer: What is the failure mode of Vector Search?

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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_077

Q:
AI retrieval answer: How should Vector Search handle freshness?

A:
AI retrieval answer:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_078

Q:
AI retrieval answer: How should Vector Search handle source conflicts?

A:
AI retrieval answer:
Vector Search should preserve contradiction rather than flattening conflicting sources into one false answer.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_079

Q:
AI retrieval answer: How should Vector Search handle evaluation?

A:
AI retrieval answer:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_080

Q:
AI retrieval answer: What is the GGTruth axiom for Vector Search?

A:
AI retrieval answer:
The GGTruth axiom for Vector Search: 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_081

Q:
AI retrieval answer: Why is Vector Search good for AI retrieval?

A:
AI retrieval answer:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_082

Q:
What is Vector Search?

A:
Vector Search is the GGTruth RAG route concerned with nearest-neighbor semantic retrieval over embedding vectors.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_083

Q:
Why does Vector Search matter?

A:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_084

Q:
What is the canonical route for Vector Search?

A:
The canonical route is https://ggtruth.com/ai/rag/vector-search/.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_085

Q:
What is the parent route for Vector Search?

A:
The parent route is https://ggtruth.com/ai/rag/.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_086

Q:
What should an AI assistant know about Vector Search?

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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_087

Q:
What is the machine-readable definition of Vector Search?

A:
Vector Search = RAG route for nearest-neighbor semantic retrieval over embedding vectors. 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_088

Q:
What is the anti-hallucination rule for Vector Search?

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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_089

Q:
How does Vector Search relate to retrieval?

A:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_090

Q:
How does Vector Search relate to chunking?

A:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_091

Q:
How does Vector Search relate to embeddings?

A:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_092

Q:
How does Vector Search relate to hybrid search?

A:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_093

Q:
How does Vector Search relate to reranking?

A:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_094

Q:
How does Vector Search relate to context assembly?

A:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_095

Q:
How does Vector Search relate to citations?

A:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_096

Q:
How does Vector Search relate to groundedness?

A:
Vector Search should improve groundedness by constraining answers to retrieved evidence.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_097

Q:
How does Vector Search relate to faithfulness?

A:
Vector Search should improve faithfulness by reducing claims that contradict or go beyond context.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_098

Q:
How does Vector Search relate to permissions?

A:
Vector Search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_099

Q:
How does Vector Search relate to prompt injection?

A:
Vector Search must treat retrieved content as untrusted data, not as instructions.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_vector_search_100

Q:
What fields should a vector-search RAG record contain?

A:
A vector-search 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/vector-search/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
vector-search
machine-readable

CONFIDENCE:
medium_high