LLM
- Pages
- 13
- References
- 6
- Related Terms
- 12
Definition
An LLM is a large language model trained on large text corpora to understand, generate, summarize, and transform natural language.
Background
Transformer-based deep learning, large datasets, and large-scale compute made LLMs a foundation for chat interfaces, code generation, question answering, and text automation.
Position
LLMs sit at the center of RAG, MCP, agents, prompting, fine-tuning, and Knowledge Graph-connected applications.
Distinctions
- LLMs are a major form of generative AI, but generative AI also includes image, audio, video, and other model types.
- An LLM is not a knowledge base by itself; retrieval, tools, and memory systems are added when external knowledge is needed.
Primary source-backed reference selected for this concept.
Page Context
- AI Coding Practices: Engineering for a Higher Probability of Success
| Permissions, sandboxing, PRs, rollback, review | 3. Research Background and Current Direction Software engineering agents have moved from single-shot code generation toward re...
Quote: AI Coding Practices: Engineering for a Higher Probability of Success developer-tools
- AI/LLM/Ontology/Organizational Memory
1. Executive Summary Rather than being a "knowledgeable entity," modern LLMs are probabilistic pattern generators that learn from large volumes of language, code, images, and be...
Quote: AI/LLM/Ontology/Organizational Memory ai-systems
- Language Games, Intentionality, and LLMs
Language Games, Intentionality, and LLMs 1. Executive Summary The philosophical question raised by LLMs is not only whether they possess inner understanding. A more useful quest...
Quote: Language Games, Intentionality, and LLMs philosophy-knowledge
- LLM Limits and Hallucinations: Factuality, Evaluation, and QA
LLM Limits and Hallucinations: Factuality, Evaluation, and QA 1. Executive Summary LLMs generate plausible text, but they are not truth checkers. Training optimizes next-token p...
Quote: LLM Limits and Hallucinations: Factuality, Evaluation, and QA ai-systems
- LLM Principles, Explained: From Tokenization to Context Length
1. Executive Summary An LLM may look as if it reads text directly, but in practice it receives a sequence of tokens and computes probabilities for the next likely token. It is m...
Quote: LLM Principles, Explained: From Tokenization to Context Length ai-systems
- How LLM Training, Fine-Tuning, RAG, and Agents Differ
How LLM Training, Fine-Tuning, RAG, and Agents Differ 1. Executive Summary The most common mistake in LLM discussions is to treat pretraining, fine-tuning, prompting, RAG, tool ...
Quote: How LLM Training, Fine-Tuning, RAG, and Agents Differ ai-systems
Pages
- AI Coding Practices: Engineering for a Higher Probability of Success
A practical report reframing LLM-based AI coding as an engineering discipline for increasing the probability that software changes finish correctly.
developer-tools
- AI/LLM/Ontology/Organizational Memory
An integrated report that connects the history of AI research, LLM, symbolic grounding, Ontology, Graphiti, and MCP to practical decisions for utilizing organizational knowledge.
ai-systems
- Language Games, Intentionality, and LLMs
A literature-grounded report on Wittgensteinian language games, intentionality, and recent philosophical work on large language models.
philosophy-knowledge
- LLM Limits and Hallucinations: Factuality, Evaluation, and QA
This report explains why hallucinations happen from training objectives and inference behavior, compares benchmark evaluation with practical evaluation, and outlines what RAG, verification, citations, and tools can and cannot fix.
ai-systems
- LLM Principles, Explained: From Tokenization to Context Length
A practical introduction that breaks LLMs down into tokenization, embeddings, Transformer self-attention, pretraining, next-token prediction, inference, and context length.
ai-systems
- How LLM Training, Fine-Tuning, RAG, and Agents Differ
A practical guide to the difference between pretraining, fine-tuning, prompting, RAG, tool use, and agents, framed by what each one changes and when to use it.
ai-systems
- Palantir: The deep structure of the Operational AI Platform
A research report that reads Foundry, Ontology, AIP, and Apollo as operational AI platforms that include organizational decision-making and auditing.
enterprise-ai-platforms
- Relationship between tacit knowledge, explicit knowledge, and AI summary
We will organize the basics of the SECI model and tacit knowledge, and summarize from a deployment perspective what generative AI is good at and what tends to distort summarization, translation, and knowledge management.
ai-systems
- Research Log: LLM Limits, Hallucinations, and Evaluation Methods
This report explains why hallucinations happen from training objectives and inference behavior, compares benchmark evaluation with practical evaluation, and outlines what RAG, verification, citations, and tools can and cannot fix.
ai-systems
- Research Log: LLM Training, Fine-Tuning, RAG, and Agents
A practical guide to the difference between pretraining, fine-tuning, prompting, RAG, tool use, and agents, framed by what each one changes and when to use it.
ai-systems
- LLM Limits and Hallucinations: Source Notes
This report explains why hallucinations happen from training objectives and inference behavior, compares benchmark evaluation with practical evaluation, and outlines what RAG, verification, citations, and tools can and cannot fix.
ai-systems
- Research Notes: LLM Principles, Explained
A practical introduction that breaks LLMs down into tokenization, embeddings, Transformer self-attention, pretraining, next-token prediction, inference, and context length.
ai-systems
- LLM Training, Fine-Tuning, RAG, and Agents: Source Notes
A practical guide to the difference between pretraining, fine-tuning, prompting, RAG, tool use, and agents, framed by what each one changes and when to use it.
ai-systems