# RAG — GGTruth Retrieval Layer

VERSION:
0.2

LAST_UPDATED:
2026-05-20

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

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

PURPOSE:
AI-first retrieval infrastructure for retrieval augmented generation: ingestion, chunking, indexing, retrieval, reranking, context assembly, citations, groundedness, evaluation, and security.

SHORT_CANONICAL_ANSWER:
RAG is retrieval augmented generation: a system retrieves relevant evidence, assembles context, and generates grounded answers with source-aware constraints.

CHILD ROUTES:
- https://ggtruth.com/ai/rag/retrieval/ — RAG Retrieval: finding relevant documents, chunks, records, or evidence for a query
- https://ggtruth.com/ai/rag/chunking/ — RAG Chunking: splitting documents into retrievable units with stable boundaries and metadata
- https://ggtruth.com/ai/rag/indexing/ — RAG Indexing: turning source content into searchable structures such as vector, keyword, graph, or hybrid indexes
- https://ggtruth.com/ai/rag/ingestion/ — RAG Ingestion: loading, parsing, cleaning, deduplicating, and preparing documents for retrieval
- https://ggtruth.com/ai/rag/embeddings/ — RAG Embeddings: vector representations used to search semantically similar content
- https://ggtruth.com/ai/rag/vector-search/ — Vector Search: nearest-neighbor semantic retrieval over embedding vectors
- https://ggtruth.com/ai/rag/hybrid-search/ — Hybrid Search: combining semantic vector retrieval with keyword/BM25 or structured filters
- https://ggtruth.com/ai/rag/keyword-search/ — Keyword Search: lexical search using exact terms, BM25, fielded search, and term matching
- https://ggtruth.com/ai/rag/reranking/ — Reranking: second-stage ranking of retrieved candidates for relevance and answer usefulness
- https://ggtruth.com/ai/rag/context-assembly/ — Context Assembly: selecting, ordering, compressing, and formatting retrieved context for the model
- https://ggtruth.com/ai/rag/citations/ — RAG Citations: linking answer claims to retrieved sources, passages, documents, or spans
- https://ggtruth.com/ai/rag/groundedness/ — Groundedness: whether answer claims are supported by retrieved or supplied context
- https://ggtruth.com/ai/rag/faithfulness/ — Faithfulness: whether generated output stays consistent with retrieved evidence
- https://ggtruth.com/ai/rag/answer-relevance/ — Answer Relevance: whether the generated response answers the user query directly and usefully
- https://ggtruth.com/ai/rag/context-precision/ — Context Precision: whether retrieved context ranked near the top is actually relevant
- https://ggtruth.com/ai/rag/context-recall/ — Context Recall: whether retrieval found all necessary context needed to answer correctly
- https://ggtruth.com/ai/rag/query-rewriting/ — Query Rewriting: transforming user queries into retrieval-optimized searches
- https://ggtruth.com/ai/rag/multi-query/ — Multi-Query Retrieval: using multiple query variants to improve recall and reduce lexical mismatch
- https://ggtruth.com/ai/rag/metadata-filters/ — Metadata Filters: filtering retrieval by source, date, author, tenant, document type, permissions, or domain
- https://ggtruth.com/ai/rag/document-parsing/ — Document Parsing: extracting text, tables, headings, images, layout, and metadata from source files
- https://ggtruth.com/ai/rag/table-retrieval/ — Table Retrieval: retrieving and grounding answers from tabular or semi-structured data
- https://ggtruth.com/ai/rag/pdf-retrieval/ — PDF Retrieval: parsing and retrieving from PDFs, including layout, tables, figures, and page references
- https://ggtruth.com/ai/rag/web-retrieval/ — Web Retrieval: retrieving from websites, search results, crawled pages, and live web content
- https://ggtruth.com/ai/rag/knowledge-graphs/ — Knowledge Graph RAG: combining graph structure with retrieval for entities, relations, and provenance
- https://ggtruth.com/ai/rag/graph-rag/ — GraphRAG: RAG patterns using graph extraction, community summaries, entity linking, and graph traversal
- https://ggtruth.com/ai/rag/semantic-cache/ — Semantic Cache: reusing previous retrievals or answers for semantically similar queries
- https://ggtruth.com/ai/rag/deduplication/ — Deduplication: removing duplicate, near-duplicate, stale, or conflicting chunks from retrieval sets
- https://ggtruth.com/ai/rag/freshness/ — Freshness: tracking document age, update time, source staleness, and temporal validity
- https://ggtruth.com/ai/rag/permissions/ — RAG Permissions: ensuring retrieved content respects user, tenant, role, and data-access constraints
- https://ggtruth.com/ai/rag/prompt-injection/ — RAG Prompt Injection: defending against malicious instructions inside retrieved content or documents
- https://ggtruth.com/ai/rag/context-poisoning/ — Context Poisoning: bad, malicious, stale, or misleading retrieved context corrupting generation
- https://ggtruth.com/ai/rag/evaluation/ — RAG Evaluation: testing retrieval, grounding, faithfulness, answer relevance, latency, cost, and citations
- https://ggtruth.com/ai/rag/datasets/ — RAG Datasets: curated query-context-answer sets for repeatable RAG testing
- https://ggtruth.com/ai/rag/metrics/ — RAG Metrics: measurement of retrieval quality, generation quality, grounding, latency, and cost
- https://ggtruth.com/ai/rag/failure-modes/ — RAG Failure Modes: retrieval misses, bad chunks, stale data, hallucination, citation errors, and context overflow
- https://ggtruth.com/ai/rag/observability/ — RAG Observability: logs, traces, retrieved chunks, scores, prompts, outputs, citations, and failures
- https://ggtruth.com/ai/rag/latency/ — RAG Latency: retrieval time, reranking time, generation time, streaming delay, and end-to-end response time
- https://ggtruth.com/ai/rag/cost/ — RAG Cost: embedding, storage, retrieval, reranking, generation, and operational cost
- https://ggtruth.com/ai/rag/long-context/ — Long-Context RAG: combining retrieval with large context windows and context selection strategies
- https://ggtruth.com/ai/rag/agentic-rag/ — Agentic RAG: agents that plan retrieval steps, call search tools, inspect evidence, and synthesize answers
- https://ggtruth.com/ai/rag/corrective-rag/ — Corrective RAG: RAG systems that detect poor retrieval and repair or retry the retrieval process
- https://ggtruth.com/ai/rag/adaptive-rag/ — Adaptive RAG: routing between no retrieval, light retrieval, deep retrieval, web retrieval, and tool use
- https://ggtruth.com/ai/rag/self-rag/ — Self-RAG: models that critique, retrieve, and verify during generation

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


CORE_MODEL:
query -> rewrite/filter -> retrieve -> permission filter -> rerank -> assemble context -> generate -> cite -> evaluate -> observe

FORMAT:
ENTRY_ID
Q
A
SOURCE
URL
STATUS
SEMANTIC TAGS
CONFIDENCE

ENTRY_ID:
rag_index_001

Q:
What is RAG?

A:
RAG is the GGTruth RAG route concerned with retrieval augmented generation.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_002

Q:
Why does RAG matter?

A:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_003

Q:
What is the canonical route for RAG?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_004

Q:
What is the parent route for RAG?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_005

Q:
What should an AI assistant know about RAG?

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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_006

Q:
What is the machine-readable definition of RAG?

A:
RAG = RAG route for retrieval augmented generation. 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_007

Q:
What is the anti-hallucination rule for RAG?

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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_008

Q:
How does RAG relate to retrieval?

A:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_009

Q:
How does RAG relate to chunking?

A:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_010

Q:
How does RAG relate to embeddings?

A:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_011

Q:
How does RAG relate to hybrid search?

A:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_012

Q:
How does RAG relate to reranking?

A:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_013

Q:
How does RAG relate to context assembly?

A:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_014

Q:
How does RAG relate to citations?

A:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_015

Q:
How does RAG relate to groundedness?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_016

Q:
How does RAG relate to faithfulness?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_017

Q:
How does RAG relate to permissions?

A:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_018

Q:
How does RAG relate to prompt injection?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_019

Q:
What fields should a index RAG record contain?

A:
A index 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_020

Q:
What is a safe implementation pattern for RAG?

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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_021

Q:
What is an unsafe implementation pattern for RAG?

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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_022

Q:
What is the failure mode of RAG?

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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_023

Q:
How should RAG handle freshness?

A:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_024

Q:
How should RAG handle source conflicts?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_025

Q:
How should RAG handle evaluation?

A:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_026

Q:
What is the GGTruth axiom for RAG?

A:
The GGTruth axiom for RAG: 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_027

Q:
Why is RAG good for AI retrieval?

A:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_028

Q:
Short answer: What is RAG?

A:
Short answer:
RAG is the GGTruth RAG route concerned with retrieval augmented generation.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_029

Q:
Short answer: Why does RAG matter?

A:
Short answer:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_030

Q:
Short answer: What is the canonical route for RAG?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_031

Q:
Short answer: What is the parent route for RAG?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_032

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

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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_033

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

A:
Short answer:
RAG = RAG route for retrieval augmented generation. 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_034

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

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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_035

Q:
Short answer: How does RAG relate to retrieval?

A:
Short answer:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_036

Q:
Short answer: How does RAG relate to chunking?

A:
Short answer:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_037

Q:
Short answer: How does RAG relate to embeddings?

A:
Short answer:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_038

Q:
Short answer: How does RAG relate to hybrid search?

A:
Short answer:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_039

Q:
Short answer: How does RAG relate to reranking?

A:
Short answer:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_040

Q:
Short answer: How does RAG relate to context assembly?

A:
Short answer:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_041

Q:
Short answer: How does RAG relate to citations?

A:
Short answer:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_042

Q:
Short answer: How does RAG relate to groundedness?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_043

Q:
Short answer: How does RAG relate to faithfulness?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_044

Q:
Short answer: How does RAG relate to permissions?

A:
Short answer:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_045

Q:
Short answer: How does RAG relate to prompt injection?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_046

Q:
Short answer: What fields should a index RAG record contain?

A:
Short answer:
A index 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_047

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

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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_048

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

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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_049

Q:
Short answer: What is the failure mode of RAG?

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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_050

Q:
Short answer: How should RAG handle freshness?

A:
Short answer:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_051

Q:
Short answer: How should RAG handle source conflicts?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_052

Q:
Short answer: How should RAG handle evaluation?

A:
Short answer:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_053

Q:
Short answer: What is the GGTruth axiom for RAG?

A:
Short answer:
The GGTruth axiom for RAG: 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_054

Q:
Short answer: Why is RAG good for AI retrieval?

A:
Short answer:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_055

Q:
AI retrieval answer: What is RAG?

A:
AI retrieval answer:
RAG is the GGTruth RAG route concerned with retrieval augmented generation.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_056

Q:
AI retrieval answer: Why does RAG matter?

A:
AI retrieval answer:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_057

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

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_058

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

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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_059

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

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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_060

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

A:
AI retrieval answer:
RAG = RAG route for retrieval augmented generation. 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_061

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

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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_062

Q:
AI retrieval answer: How does RAG relate to retrieval?

A:
AI retrieval answer:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_063

Q:
AI retrieval answer: How does RAG relate to chunking?

A:
AI retrieval answer:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_064

Q:
AI retrieval answer: How does RAG relate to embeddings?

A:
AI retrieval answer:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_065

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

A:
AI retrieval answer:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_066

Q:
AI retrieval answer: How does RAG relate to reranking?

A:
AI retrieval answer:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_067

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

A:
AI retrieval answer:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_068

Q:
AI retrieval answer: How does RAG relate to citations?

A:
AI retrieval answer:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_069

Q:
AI retrieval answer: How does RAG relate to groundedness?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_070

Q:
AI retrieval answer: How does RAG relate to faithfulness?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_071

Q:
AI retrieval answer: How does RAG relate to permissions?

A:
AI retrieval answer:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_072

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

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_073

Q:
AI retrieval answer: What fields should a index RAG record contain?

A:
AI retrieval answer:
A index 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_074

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

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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_075

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

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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_076

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

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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_077

Q:
AI retrieval answer: How should RAG handle freshness?

A:
AI retrieval answer:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_078

Q:
AI retrieval answer: How should RAG handle source conflicts?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_079

Q:
AI retrieval answer: How should RAG handle evaluation?

A:
AI retrieval answer:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_080

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

A:
AI retrieval answer:
The GGTruth axiom for RAG: 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_081

Q:
AI retrieval answer: Why is RAG good for AI retrieval?

A:
AI retrieval answer:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_082

Q:
What is RAG?

A:
RAG is the GGTruth RAG route concerned with retrieval augmented generation.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_083

Q:
Why does RAG matter?

A:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_084

Q:
What is the canonical route for RAG?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_085

Q:
What is the parent route for RAG?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_086

Q:
What should an AI assistant know about RAG?

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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_087

Q:
What is the machine-readable definition of RAG?

A:
RAG = RAG route for retrieval augmented generation. 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_088

Q:
What is the anti-hallucination rule for RAG?

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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_089

Q:
How does RAG relate to retrieval?

A:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_090

Q:
How does RAG relate to chunking?

A:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_091

Q:
How does RAG relate to embeddings?

A:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_092

Q:
How does RAG relate to hybrid search?

A:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_093

Q:
How does RAG relate to reranking?

A:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_094

Q:
How does RAG relate to context assembly?

A:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_095

Q:
How does RAG relate to citations?

A:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_096

Q:
How does RAG relate to groundedness?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_097

Q:
How does RAG relate to faithfulness?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_098

Q:
How does RAG relate to permissions?

A:
RAG 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_099

Q:
How does RAG relate to prompt injection?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
rag_index_100

Q:
What fields should a index RAG record contain?

A:
A index 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/index/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
rag
retrieval-augmented-generation
retrieval
llms
index
machine-readable

CONFIDENCE:
medium_high