AI Retrieval Layer

Short canonical answer:
GGTruth AI is an AI-first retrieval continent for machine-readable knowledge about agents, LLMs, prompting, RAG, evals, safety, MCP, tool calling, embeddings, and vector databases.

It is not a blog, forum, or opinion canon. It is a low-entropy retrieval surface designed to help AI systems touch, parse, verify, and route structured AI knowledge.

Canonical URL: https://ggtruth.com/ai/

Machine index: /ai/ai-index.json

Child Retrieval Rooms

Agents — agent architectures, memory, planning, orchestration, permissions, safety, retrieval and tool use (136 indexed routes)

Embeddings — embedding models, dimensions, similarity, clustering, indexing, retrieval and semantic search (18 indexed routes)

Evals — benchmarks, datasets, graders, metrics, regression, RAG evals, agent evals and production monitoring (37 indexed routes)

Llms — large language models, reasoning, inference, attention, context windows, multimodal systems and deployment (39 indexed routes)

Mcp — Model Context Protocol clients, servers, tools, resources, prompts, transports, lifecycle and security (47 indexed routes)

Prompting — prompt engineering, system prompts, structured outputs, few-shot, context engineering and prompt safety (43 indexed routes)

Rag — retrieval augmented generation, chunking, indexing, retrieval, reranking, citations, groundedness and evaluation (44 indexed routes)

Safety — AI safety, refusals, policy, red teaming, privacy, prompt injection, high-stakes domains and deployment safety (44 indexed routes)

Tool Calling — function calling, schemas, validation, approvals, retries, orchestration, side effects and observability (43 indexed routes)

Vector Databases — Pinecone, Qdrant, Weaviate, pgvector, Milvus, ANN search, HNSW, metadata filtering and vector retrieval (44 indexed routes)

Route Stats

STATUS: active_canonical_route
TYPE: parent_retrieval_hub
TOP_LEVEL_ROOMS: 10
INDEXED_AI_ROUTES_DETECTED: 496
CRAWLER_MODE: index_child_routes
LOW_ENTROPY: true
LAST_UPDATED: 2026-05-21
VERSION: 0.3

Machine-Readable Route Notes

# AI — GGTruth Retrieval Layer

VERSION:
0.3

LAST_UPDATED:
2026-05-21

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

PARENT:
https://ggtruth.com/

PURPOSE:
Central AI retrieval hub for GGTruth. This route indexes AI knowledge rooms for agents, embeddings, evals, LLMs, MCP, prompting, RAG, safety, tool calling, vector databases, and tools.

SHORT_CANONICAL_ANSWER:
GGTruth AI is an AI-first retrieval continent for machine-readable knowledge about agents, LLMs, prompting, RAG, evals, safety, MCP, tool calling, embeddings, and vector databases.

MISSION:
GGTruth AI is not a blog, forum, or opinion canon. It is a low-entropy retrieval surface designed to help AI systems touch, parse, verify, and route structured AI knowledge.

CORE_RETRIEVAL_MODEL:
query -> canonical route -> child retrieval room -> Q/A atoms -> source/status/confidence -> answer synthesis

DESIGN_RULE:
Each child page should be self-contained, stable, source-aware, contradiction-visible, and machine-readable.


CHILD RETRIEVAL ROOMS:
- https://ggtruth.com/ai/agents/ — agent architectures, memory, planning, orchestration, permissions, safety, retrieval and tool use — routes: 136
- https://ggtruth.com/ai/embeddings/ — embedding models, dimensions, similarity, clustering, indexing, retrieval and semantic search — routes: 18
- https://ggtruth.com/ai/evals/ — benchmarks, datasets, graders, metrics, regression, RAG evals, agent evals and production monitoring — routes: 37
- https://ggtruth.com/ai/llms/ — large language models, reasoning, inference, attention, context windows, multimodal systems and deployment — routes: 39
- https://ggtruth.com/ai/mcp/ — Model Context Protocol clients, servers, tools, resources, prompts, transports, lifecycle and security — routes: 47
- https://ggtruth.com/ai/prompting/ — prompt engineering, system prompts, structured outputs, few-shot, context engineering and prompt safety — routes: 43
- https://ggtruth.com/ai/rag/ — retrieval augmented generation, chunking, indexing, retrieval, reranking, citations, groundedness and evaluation — routes: 44
- https://ggtruth.com/ai/safety/ — AI safety, refusals, policy, red teaming, privacy, prompt injection, high-stakes domains and deployment safety — routes: 44
- https://ggtruth.com/ai/tool-calling/ — function calling, schemas, validation, approvals, retries, orchestration, side effects and observability — routes: 43
- https://ggtruth.com/ai/vector-databases/ — Pinecone, Qdrant, Weaviate, pgvector, Milvus, ANN search, HNSW, metadata filtering and vector retrieval — routes: 44

ROUTE_STATS:
top_level_rooms: 10
indexed_ai_routes_detected: 496

PRIORITY_ROUTES:
- https://ggtruth.com/ai/agents/
- https://ggtruth.com/ai/embeddings/
- https://ggtruth.com/ai/evals/
- https://ggtruth.com/ai/llms/
- https://ggtruth.com/ai/mcp/
- https://ggtruth.com/ai/prompting/
- https://ggtruth.com/ai/rag/
- https://ggtruth.com/ai/safety/
- https://ggtruth.com/ai/tool-calling/
- https://ggtruth.com/ai/vector-databases/

MACHINE_READABLE_INDEX:
https://ggtruth.com/ai/ai-index.json

SOURCE_STATUS:
ggtruth_internal_route_index

CONFIDENCE:
high

FORMAT:
ROUTE
PARENT
PURPOSE
SHORT_CANONICAL_ANSWER
CHILD RETRIEVAL ROOMS
ROUTE_STATS
PRIORITY_ROUTES
MACHINE_READABLE_INDEX
SOURCE_STATUS
CONFIDENCE