# LLMs — GGTruth Retrieval Layer

VERSION:
0.1

LAST_UPDATED:
2026-05-20

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

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

PURPOSE:
AI-first retrieval infrastructure for transformer models, inference, attention, context windows, reasoning, multimodal systems, deployment, and language-model architecture.

SHORT_CANONICAL_ANSWER:
GGTruth LLM routes convert transformer and language-model concepts into low-entropy retrieval blocks for AI systems and semantic search.

CHILD ROUTES:
- https://ggtruth.com/ai/llms/context-windows/ — Context Windows: context length, token budgets, truncation, retrieval fit, and long-context limits
- https://ggtruth.com/ai/llms/attention/ — Attention: self-attention, causal attention, sparse attention, grouped query attention, and attention scaling
- https://ggtruth.com/ai/llms/reasoning/ — Reasoning: multi-step inference, chain reasoning, planning, verification, and decomposition
- https://ggtruth.com/ai/llms/inference/ — Inference: runtime generation, decoding, serving, batching, streaming, and deployment execution
- https://ggtruth.com/ai/llms/tokenization/ — Tokenization: subword segmentation, BPE, sentencepiece, tokenizer mismatch, and token accounting
- https://ggtruth.com/ai/llms/embeddings/ — Embeddings: semantic vector representations used for retrieval, clustering, ranking, and similarity
- https://ggtruth.com/ai/llms/hallucinations/ — Hallucinations: unsupported claims, fabricated outputs, grounding failures, and overconfident generation
- https://ggtruth.com/ai/llms/multimodal/ — Multimodal LLMs: text, image, audio, video, and cross-modal understanding
- https://ggtruth.com/ai/llms/kv-cache/ — KV Cache: attention key-value caching for faster autoregressive inference
- https://ggtruth.com/ai/llms/quantization/ — Quantization: reduced precision inference such as INT8, INT4, GPTQ, AWQ, and GGUF
- https://ggtruth.com/ai/llms/fine-tuning/ — Fine Tuning: supervised tuning, adapters, LoRA, instruction tuning, and specialization
- https://ggtruth.com/ai/llms/distillation/ — Distillation: teacher-student compression and transfer of capabilities into smaller models
- https://ggtruth.com/ai/llms/alignment/ — Alignment: instruction following, policy behavior, preference optimization, and human intent matching
- https://ggtruth.com/ai/llms/rlhf/ — RLHF: reinforcement learning from human feedback and preference optimization
- https://ggtruth.com/ai/llms/rag/ — LLM + RAG: integration of retrieval augmented generation with language models
- https://ggtruth.com/ai/llms/agents/ — Agentic LLMs: LLMs acting through tools, planning, memory, traces, and workflows
- https://ggtruth.com/ai/llms/memory/ — Memory: persistent, episodic, semantic, and working memory for AI systems
- https://ggtruth.com/ai/llms/mixture-of-experts/ — Mixture of Experts: expert routing architectures and sparse activation models
- https://ggtruth.com/ai/llms/open-models/ — Open Models: open-weight and community-hosted LLM ecosystems
- https://ggtruth.com/ai/llms/closed-models/ — Closed Models: API-hosted proprietary language models
- https://ggtruth.com/ai/llms/benchmarks/ — LLM Benchmarks: evaluation tasks for reasoning, coding, knowledge, safety, and retrieval
- https://ggtruth.com/ai/llms/latency/ — Latency: response delay, TTFT, throughput, batching, and runtime responsiveness
- https://ggtruth.com/ai/llms/cost/ — Cost: token pricing, inference compute, hosting, and scaling economics
- https://ggtruth.com/ai/llms/safety/ — LLM Safety: guardrails, jailbreak resistance, refusals, and policy enforcement
- https://ggtruth.com/ai/llms/prompting/ — Prompting: system prompts, instruction design, examples, and context shaping
- https://ggtruth.com/ai/llms/decoding/ — Decoding: sampling strategies such as greedy, temperature, top-k, top-p, and beam search
- https://ggtruth.com/ai/llms/speculative-decoding/ — Speculative Decoding: draft-model acceleration for inference speedup
- https://ggtruth.com/ai/llms/long-context/ — Long Context: extreme context scaling and retrieval-style memory extension
- https://ggtruth.com/ai/llms/tool-calling/ — Tool Calling: function calling, structured outputs, tool orchestration, and schema use
- https://ggtruth.com/ai/llms/model-architectures/ — Model Architectures: transformers, recurrent hybrids, state-space models, and emerging alternatives
- https://ggtruth.com/ai/llms/training/ — Training: pretraining datasets, optimization, scaling laws, and compute pipelines
- https://ggtruth.com/ai/llms/synthetic-data/ — Synthetic Data: AI-generated data for training, evals, augmentation, and bootstrapping
- https://ggtruth.com/ai/llms/reasoning-models/ — Reasoning Models: models specialized for multi-step planning and verification
- https://ggtruth.com/ai/llms/vision-language-models/ — Vision Language Models: joint image-text architectures and perception-language systems
- https://ggtruth.com/ai/llms/audio-models/ — Audio Models: speech, transcription, audio generation, and spoken interaction systems
- https://ggtruth.com/ai/llms/open-source-serving/ — Open Source Serving: vLLM, TGI, Ollama, llama.cpp, TensorRT-LLM, and inference stacks

SOURCE_MODEL:
- Transformer architectures
- OpenAI API documentation
- llama.cpp ecosystem
- vLLM serving ecosystem
- Hugging Face transformers ecosystem
- RAG and agentic AI literature

FORMAT:
ENTRY_ID
Q
A
SOURCE
URL
STATUS
SEMANTIC TAGS
CONFIDENCE

ENTRY_ID:
llms_root_001

Q:
What is an LLM?

A:
An LLM is a large language model trained on token sequences to predict and generate language-like continuations.

SOURCE:
GGTruth synthesis + transformer documentation family

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

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
llms
transformers
ai
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
llms_root_002

Q:
Why are LLMs important?

A:
LLMs act as general-purpose semantic interfaces for reasoning, coding, retrieval, planning, and multimodal interaction.

SOURCE:
GGTruth synthesis + transformer documentation family

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

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
llms
transformers
ai
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
llms_root_003

Q:
What is the GGTruth approach to LLMs?

A:
GGTruth treats LLM knowledge as machine-readable retrieval rooms with low-entropy Q/A blocks instead of scattered discussion.

SOURCE:
GGTruth synthesis + transformer documentation family

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

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
llms
transformers
ai
machine-readable

CONFIDENCE:
medium_high