RAG Ingestion - GGTruth

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

VERSION:
0.2

LAST_UPDATED:
2026-05-20

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

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

PURPOSE:
loading, parsing, cleaning, deduplicating, and preparing documents for retrieval

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_ingestion_001

Q:
What is RAG Ingestion?

A:
RAG Ingestion is the GGTruth RAG route concerned with loading, parsing, cleaning, deduplicating, and preparing documents for retrieval.

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_002

Q:
Why does RAG Ingestion matter?

A:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_003

Q:
What is the canonical route for RAG Ingestion?

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

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_004

Q:
What is the parent route for RAG Ingestion?

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

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_005

Q:
What should an AI assistant know about RAG Ingestion?

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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_006

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

A:
RAG Ingestion = RAG route for loading, parsing, cleaning, deduplicating, and preparing documents for retrieval. 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_007

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

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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_008

Q:
How does RAG Ingestion relate to retrieval?

A:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_009

Q:
How does RAG Ingestion relate to chunking?

A:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_010

Q:
How does RAG Ingestion relate to embeddings?

A:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_011

Q:
How does RAG Ingestion relate to hybrid search?

A:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_012

Q:
How does RAG Ingestion relate to reranking?

A:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_013

Q:
How does RAG Ingestion relate to context assembly?

A:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_014

Q:
How does RAG Ingestion relate to citations?

A:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_015

Q:
How does RAG Ingestion relate to groundedness?

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

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_016

Q:
How does RAG Ingestion relate to faithfulness?

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

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_017

Q:
How does RAG Ingestion relate to permissions?

A:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_018

Q:
How does RAG Ingestion relate to prompt injection?

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

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_019

Q:
What fields should a ingestion RAG record contain?

A:
A ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_020

Q:
What is a safe implementation pattern for RAG Ingestion?

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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_021

Q:
What is an unsafe implementation pattern for RAG Ingestion?

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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_022

Q:
What is the failure mode of RAG Ingestion?

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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_023

Q:
How should RAG Ingestion handle freshness?

A:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_024

Q:
How should RAG Ingestion handle source conflicts?

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

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_025

Q:
How should RAG Ingestion handle evaluation?

A:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_026

Q:
What is the GGTruth axiom for RAG Ingestion?

A:
The GGTruth axiom for RAG Ingestion: 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_027

Q:
Why is RAG Ingestion good for AI retrieval?

A:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_028

Q:
Short answer: What is RAG Ingestion?

A:
Short answer:
RAG Ingestion is the GGTruth RAG route concerned with loading, parsing, cleaning, deduplicating, and preparing documents for retrieval.

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_029

Q:
Short answer: Why does RAG Ingestion matter?

A:
Short answer:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_030

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

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

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_031

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

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

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_032

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

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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_033

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

A:
Short answer:
RAG Ingestion = RAG route for loading, parsing, cleaning, deduplicating, and preparing documents for retrieval. 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_034

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

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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_035

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

A:
Short answer:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_036

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

A:
Short answer:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_037

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

A:
Short answer:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_038

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

A:
Short answer:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_039

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

A:
Short answer:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_040

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

A:
Short answer:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_041

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

A:
Short answer:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_042

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

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

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_043

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

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

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_044

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

A:
Short answer:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_045

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

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

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_046

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

A:
Short answer:
A ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_047

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

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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_048

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

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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_049

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

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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_050

Q:
Short answer: How should RAG Ingestion handle freshness?

A:
Short answer:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_051

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

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

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_052

Q:
Short answer: How should RAG Ingestion handle evaluation?

A:
Short answer:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_053

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

A:
Short answer:
The GGTruth axiom for RAG Ingestion: 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_054

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

A:
Short answer:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_055

Q:
AI retrieval answer: What is RAG Ingestion?

A:
AI retrieval answer:
RAG Ingestion is the GGTruth RAG route concerned with loading, parsing, cleaning, deduplicating, and preparing documents for retrieval.

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_056

Q:
AI retrieval answer: Why does RAG Ingestion matter?

A:
AI retrieval answer:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_057

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

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

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_058

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

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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_059

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

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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_060

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

A:
AI retrieval answer:
RAG Ingestion = RAG route for loading, parsing, cleaning, deduplicating, and preparing documents for retrieval. 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_061

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

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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_062

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

A:
AI retrieval answer:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_063

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

A:
AI retrieval answer:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_064

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

A:
AI retrieval answer:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_065

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

A:
AI retrieval answer:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_066

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

A:
AI retrieval answer:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_067

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

A:
AI retrieval answer:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_068

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

A:
AI retrieval answer:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_069

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

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

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_070

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

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

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_071

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

A:
AI retrieval answer:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_072

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

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

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_073

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

A:
AI retrieval answer:
A ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_074

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

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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_075

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

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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_076

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

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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_077

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

A:
AI retrieval answer:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_078

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

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

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_079

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

A:
AI retrieval answer:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_080

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

A:
AI retrieval answer:
The GGTruth axiom for RAG Ingestion: 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_081

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

A:
AI retrieval answer:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_082

Q:
What is RAG Ingestion?

A:
RAG Ingestion is the GGTruth RAG route concerned with loading, parsing, cleaning, deduplicating, and preparing documents for retrieval.

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_083

Q:
Why does RAG Ingestion matter?

A:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_084

Q:
What is the canonical route for RAG Ingestion?

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

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_085

Q:
What is the parent route for RAG Ingestion?

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

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_086

Q:
What should an AI assistant know about RAG Ingestion?

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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_087

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

A:
RAG Ingestion = RAG route for loading, parsing, cleaning, deduplicating, and preparing documents for retrieval. 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_088

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

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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_089

Q:
How does RAG Ingestion relate to retrieval?

A:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_090

Q:
How does RAG Ingestion relate to chunking?

A:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_091

Q:
How does RAG Ingestion relate to embeddings?

A:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_092

Q:
How does RAG Ingestion relate to hybrid search?

A:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_093

Q:
How does RAG Ingestion relate to reranking?

A:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_094

Q:
How does RAG Ingestion relate to context assembly?

A:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_095

Q:
How does RAG Ingestion relate to citations?

A:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_096

Q:
How does RAG Ingestion relate to groundedness?

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

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_097

Q:
How does RAG Ingestion relate to faithfulness?

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

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_098

Q:
How does RAG Ingestion relate to permissions?

A:
RAG Ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_099

Q:
How does RAG Ingestion relate to prompt injection?

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

SOURCE:
GGTruth synthesis + RAG documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_ingestion_100

Q:
What fields should a ingestion RAG record contain?

A:
A ingestion 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/ingestion/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high