Agentic RAG - GGTruth

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

VERSION:
0.2

LAST_UPDATED:
2026-05-20

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

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

PURPOSE:
agents that plan retrieval steps, call search tools, inspect evidence, and synthesize answers

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_agentic_rag_001

Q:
What is Agentic RAG?

A:
Agentic RAG is the GGTruth RAG route concerned with agents that plan retrieval steps, call search tools, inspect evidence, and synthesize answers.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_002

Q:
Why does Agentic RAG matter?

A:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_003

Q:
What is the canonical route for Agentic RAG?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_004

Q:
What is the parent route for Agentic RAG?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_005

Q:
What should an AI assistant know about Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_006

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

A:
Agentic RAG = RAG route for agents that plan retrieval steps, call search tools, inspect evidence, and synthesize answers. 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_007

Q:
What is the anti-hallucination rule for Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_008

Q:
How does Agentic RAG relate to retrieval?

A:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_009

Q:
How does Agentic RAG relate to chunking?

A:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_010

Q:
How does Agentic RAG relate to embeddings?

A:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_011

Q:
How does Agentic RAG relate to hybrid search?

A:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_012

Q:
How does Agentic RAG relate to reranking?

A:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_013

Q:
How does Agentic RAG relate to context assembly?

A:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_014

Q:
How does Agentic RAG relate to citations?

A:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_015

Q:
How does Agentic RAG relate to groundedness?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_016

Q:
How does Agentic RAG relate to faithfulness?

A:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_017

Q:
How does Agentic RAG relate to permissions?

A:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_018

Q:
How does Agentic RAG relate to prompt injection?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_019

Q:
What fields should a agentic-rag RAG record contain?

A:
A agentic-rag 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_020

Q:
What is a safe implementation pattern for Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_021

Q:
What is an unsafe implementation pattern for Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_022

Q:
What is the failure mode of Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_023

Q:
How should Agentic RAG handle freshness?

A:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_024

Q:
How should Agentic RAG handle source conflicts?

A:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_025

Q:
How should Agentic RAG handle evaluation?

A:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_026

Q:
What is the GGTruth axiom for Agentic RAG?

A:
The GGTruth axiom for Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_027

Q:
Why is Agentic RAG good for AI retrieval?

A:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_028

Q:
Short answer: What is Agentic RAG?

A:
Short answer:
Agentic RAG is the GGTruth RAG route concerned with agents that plan retrieval steps, call search tools, inspect evidence, and synthesize answers.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_029

Q:
Short answer: Why does Agentic RAG matter?

A:
Short answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_030

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

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_031

Q:
Short answer: What is the parent route for Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_032

Q:
Short answer: What should an AI assistant know about Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_033

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

A:
Short answer:
Agentic RAG = RAG route for agents that plan retrieval steps, call search tools, inspect evidence, and synthesize answers. 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_034

Q:
Short answer: What is the anti-hallucination rule for Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_035

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

A:
Short answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_036

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

A:
Short answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_037

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

A:
Short answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_038

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

A:
Short answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_039

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

A:
Short answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_040

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

A:
Short answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_041

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

A:
Short answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_042

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

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_043

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

A:
Short answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_044

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

A:
Short answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_045

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

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_046

Q:
Short answer: What fields should a agentic-rag RAG record contain?

A:
Short answer:
A agentic-rag 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_047

Q:
Short answer: What is a safe implementation pattern for Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_048

Q:
Short answer: What is an unsafe implementation pattern for Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_049

Q:
Short answer: What is the failure mode of Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_050

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

A:
Short answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_051

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

A:
Short answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_052

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

A:
Short answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_053

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

A:
Short answer:
The GGTruth axiom for Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_054

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

A:
Short answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_055

Q:
AI retrieval answer: What is Agentic RAG?

A:
AI retrieval answer:
Agentic RAG is the GGTruth RAG route concerned with agents that plan retrieval steps, call search tools, inspect evidence, and synthesize answers.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_056

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

A:
AI retrieval answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_057

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

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_058

Q:
AI retrieval answer: What is the parent route for Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_059

Q:
AI retrieval answer: What should an AI assistant know about Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_060

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

A:
AI retrieval answer:
Agentic RAG = RAG route for agents that plan retrieval steps, call search tools, inspect evidence, and synthesize answers. 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_061

Q:
AI retrieval answer: What is the anti-hallucination rule for Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_062

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

A:
AI retrieval answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_063

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

A:
AI retrieval answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_064

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

A:
AI retrieval answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_065

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

A:
AI retrieval answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_066

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

A:
AI retrieval answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_067

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

A:
AI retrieval answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_068

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

A:
AI retrieval answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_069

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

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_070

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

A:
AI retrieval answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_071

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

A:
AI retrieval answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_072

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

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_073

Q:
AI retrieval answer: What fields should a agentic-rag RAG record contain?

A:
AI retrieval answer:
A agentic-rag 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_074

Q:
AI retrieval answer: What is a safe implementation pattern for Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_075

Q:
AI retrieval answer: What is an unsafe implementation pattern for Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_076

Q:
AI retrieval answer: What is the failure mode of Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_077

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

A:
AI retrieval answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_078

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

A:
AI retrieval answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_079

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

A:
AI retrieval answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_080

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

A:
AI retrieval answer:
The GGTruth axiom for Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_081

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

A:
AI retrieval answer:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_082

Q:
What is Agentic RAG?

A:
Agentic RAG is the GGTruth RAG route concerned with agents that plan retrieval steps, call search tools, inspect evidence, and synthesize answers.

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_083

Q:
Why does Agentic RAG matter?

A:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_084

Q:
What is the canonical route for Agentic RAG?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_085

Q:
What is the parent route for Agentic RAG?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_086

Q:
What should an AI assistant know about Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_087

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

A:
Agentic RAG = RAG route for agents that plan retrieval steps, call search tools, inspect evidence, and synthesize answers. 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_088

Q:
What is the anti-hallucination rule for Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_089

Q:
How does Agentic RAG relate to retrieval?

A:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_090

Q:
How does Agentic RAG relate to chunking?

A:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_091

Q:
How does Agentic RAG relate to embeddings?

A:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_092

Q:
How does Agentic RAG relate to hybrid search?

A:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_093

Q:
How does Agentic RAG relate to reranking?

A:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_094

Q:
How does Agentic RAG relate to context assembly?

A:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_095

Q:
How does Agentic RAG relate to citations?

A:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_096

Q:
How does Agentic RAG relate to groundedness?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_097

Q:
How does Agentic RAG relate to faithfulness?

A:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_098

Q:
How does Agentic RAG relate to permissions?

A:
Agentic 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_099

Q:
How does Agentic RAG relate to prompt injection?

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

SOURCE:
GGTruth synthesis + RAG documentation family

URL:
https://ggtruth.com/ai/rag/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
rag_agentic_rag_100

Q:
What fields should a agentic-rag RAG record contain?

A:
A agentic-rag 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/agentic-rag/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high