# AI and Context Engineering Glossary Unified terminology reference for AI, Context Engineering, and Knowledge Management. Organized by domain. Each entry links to its dedicated vault note. ## AI Fundamentals - [[Artificial Intelligence (AI)]] -- machines performing tasks requiring human intelligence - [[Machine Learning (ML)]] -- systems learning from data - [[Neural Networks (NNs)]] -- computing systems inspired by biological brains - [[Deep Learning]] -- multi-layer neural networks - [[Natural Language Processing (NLP)]] -- machines understanding human language - [[Large Language Models (LLMs)]] -- massive text-trained neural networks - [[Small Language Models (SLMs)]] -- compact LLMs for local/edge use - [[Generative AI (Gen AI)]] -- AI that creates new content - [[AI Multimodal]] -- AI processing multiple input types - [[AI Frontier Model]] -- the most capable models available - [[AI Open Weight Models]] -- models with publicly available weights - [[AI Literacy]] -- understanding AI well enough to use it effectively ## How AI Works - [[AI Tokenization]] -- breaking text into tokens - [[Context Window]] -- maximum tokens per interaction - [[AI Attention]] -- mechanism for focusing on relevant input - [[Transformers]] -- the architecture behind modern LLMs - [[AI Foundation Models]] -- large pre-trained models - [[AI Inference]] -- running a trained model - [[Embeddings]] -- vector representations of meaning - [[AI Scaling Laws]] -- compute/data/performance relationships - [[AI Mixture of Experts (MoE)]] -- activating parameter subsets - [[AI KV Cache]] -- inference memory optimization - [[AI Speculative Decoding]] -- parallel token verification - [[Knowledge Distillation]] -- compressing models - [[Diffusion Models]] -- image generation via noise reversal ## Training and Customization - [[AI Fine-Tuning]] -- adapting models to specific tasks - [[AI Instruction Tuning]] -- training to follow instructions - [[Reinforcement Learning From Human Feedback (RLHF)]] -- aligning with human preferences - [[Low Rank Adapter (LoRA)]] -- efficient fine-tuning via adapters - [[AI Quantization]] -- reducing model precision for efficiency - [[Synthetic data]] -- artificially generated training data - [[AI Sampling Parameters]] -- controlling token selection (top-p, top-k) - [[AI Temperature]] -- controlling output randomness ## Using AI - [[AI Assistants]] -- conversational AI tools (ChatGPT, Claude, Gemini) - [[Levels of AI use]] -- progression from chat to workflow - [[AI Model Selection]] -- choosing the right model - [[AI Prompts]] -- instructions given to AI - [[Prompt Engineering]] -- crafting effective prompts - [[Prompt Engineering Strategies]] -- few-shot, CoT, role-playing, etc. - [[Prompt Engineering Best Practices]] -- proven patterns - [[Prompt Chaining]] -- sequential prompt pipelines - [[Chain-of-Thought (CoT) prompting]] -- step-by-step reasoning - [[AI Hallucination]] -- confident false generation - [[AI Sycophancy]] -- agreeing instead of being honest - [[AI Bias]] -- systematic errors from data/prompts/agents - [[Cognitive debt]] -- hidden cost of not understanding AI output - [[Human-AI Collaboration Patterns]] -- five ways humans and AI work together - [[AI and Trust]] -- calibrating when to trust AI - [[AI Privacy]] -- data exposure when using AI - [[AI Training Data Collection]] -- providers using your data - [[Running AI Models Locally]] -- self-hosted inference - [[AI Without Code]] -- AI for non-technical users - [[AI-Augmented Daily Workflow]] -- what an AI-powered day looks like ## AI Agents - [[AI Agents]] -- autonomous AI systems with tools and goals - [[Distinction between AI Agents and Automation Workflows]] -- agents reason; automation follows rules - [[Agents Mental Model]] -- how to think about agents - [[Agentic loops]] -- observe-think-act cycle - [[AI Agent Identity]] -- role, personality, expertise - [[AI Agent Memory]] -- persistence across sessions - [[AI Agent Skills]] -- codified procedures - [[AI Agent Routing]] -- directing to the right agent - [[AI Agent Harness]] -- infrastructure controlling agents - [[AI Agent Permissions]] -- controlling what agents can do - [[AI Subagents]] -- child agents for subtasks - [[AI Agent Panels]] -- multi-angle evaluation groups - [[AI Agent Orchestration]] -- coordinating multiple agents - [[Multi-Agent System (MAS)]] -- collaborative agent architectures - [[AI Agent Swarms]] -- large-scale parallel coordination - [[AI Instruction Drift]] -- agents deviating over time - [[Lethal Trifecta for AI Agents]] -- hallucination + tools + autonomy - [[Receptionist AI Design Pattern]] -- intent classification and routing - [[Prompt Lazy Loading AI Design Pattern (PLL)]] -- on-demand context loading ## Skill and Agent Engineering - [[AI Skill Best Practices]] -- lean, resilient, well-scoped - [[AI Skill Composability]] -- building complex from simple - [[AI Skill Scoping]] -- user vs project vs team vs public - [[AI Skill Distribution]] -- sharing across projects/teams - [[AI Agent Distribution]] -- packaging complete agents - [[AI Skill Portability]] -- working across platforms - [[AI Agent Portability]] -- identity portable, runtime not - [[AI Interoperability]] -- transparent model/provider switching - [[AI Skill Resilience]] -- no hardcoded assumptions - [[AI Skill Versioning]] -- managing skill changes - [[AI Skill Testing]] -- validating non-deterministic output - [[AI Skill Supply Chain Security]] -- skills are code - [[Agentic Engineering]] -- discipline of building agent systems - [[Agent System Engineering]] -- full-stack agent engineering ## Context Engineering - [[Context Engineering]] -- designing information AI receives - [[Context Reduces AI Entropy]] -- more context = less variability - [[Types of Context for AI Agents]] -- system prompts, memory, skills, identity - [[Context Budget]] -- finite context allocation - [[Token Budget]] -- hard token limits - [[Context Layering]] -- organizing by priority - [[Context Anchoring]] -- pinning critical context - [[Context Provenance]] -- tracking context origin - [[Context Compression]] -- saying more in fewer tokens - [[Context Signal-to-Noise Ratio]] -- useful vs noise ratio - [[Context Lifecycle]] -- creation to retirement - [[Context Drift]] -- gradual staleness - [[Context Hygiene]] -- keeping context clean - [[Context Bloat]] -- too much low-value context - [[Context Entropy]] -- natural tendency toward disorder - [[AI Context Rot]] -- silent decay over time - [[Context Poisoning]] -- corrupted context - [[Context Confusion]] -- contradictory context - [[Context Distraction]] -- irrelevant context - [[Context Isolation]] -- separating contexts - [[Context-as-Code]] -- version-controlled context (CLAUDE.md, AGENTS.md) - [[Context File Hierarchy]] -- nested directory composition - [[Intent Engineering]] -- ensuring AI understands actual intent - [[Harness Engineering]] -- system-level infrastructure - [[Agentic Context Engineering]] -- CE for autonomous agents - [[AI Master Prompt]] -- comprehensive interaction definition - [[How to structure your AI Master Prompt]] -- practical guide ## Context Management Hierarchy - [[Levels of AI Context Management]] -- zero to mastery progression - [[Context Management Maturity Model]] -- assessment framework - [[Context Inheritance]] -- how context flows down layers - [[Personal Context Management (PCM)]] -- individual level - [[Team Context Management (TCM)]] -- team level - [[Project Context Management (PCM)|Project Context Management]] -- project level - [[Enterprise Context Management (ECM)]] -- organization level ## Knowledge Management - [[Knowledge Management (KM)]] -- the discipline - [[Personal Knowledge Management (PKM)]] -- managing your own knowledge - [[Personal Knowledge Management System (PKMS)]] -- tools and systems - [[Enterprise Knowledge Management (EKM)]] -- organizational scale - [[Agentic Knowledge Management (AKM)]] -- AI agents as knowledge workers - [[Knowledge-Context Pipeline]] -- the KM to CE to AI virtuous cycle - [[Knowledge ROI]] -- return on knowledge investment - [[PKM-to-AI Readiness]] -- readiness assessment - [[AI-Ready Second Brain]] -- PKM architecture for AI - [[Atomic notes]] -- one idea per note - [[Knowledge Graph (KG)]] -- structured linked knowledge - [[Single Source of Truth (SSOT)]] -- one authoritative version - [[Knowledge Decay]] -- knowledge becoming outdated - [[Periodic reviews]] -- maintenance practice - [[Fourth place]] -- a space to think deeply ## AI Safety, Ethics, and Governance - [[AI Safety]] -- ensuring intended behavior - [[AI Alignment]] -- matching human values - [[AI Ethics]] -- fairness, transparency, accountability - [[AI Governance]] -- policies and oversight - [[AI Context Governance]] -- governing context management - [[Responsible AI]] -- fairness, transparency, accountability in practice - [[AI Usage Policy]] -- organizational AI rules - [[AI Data Security]] -- protecting data in AI systems - [[AI Agent Permissions]] -- controlling agent access - [[AI Guardrails]] -- preventing harmful output - [[Prompt injection]] -- tricking AI to ignore instructions - [[Human-in-the-Loop]] -- human approval before execution - [[AI Risks and Fears]] -- skill atrophy, job fears, over-reliance - [[Shadow AI]] -- unapproved AI tool usage - [[EU AI Act]] -- European AI regulation - [[Constitutional AI]] -- self-evaluating AI principles - [[Data Poisoning]] -- corrupting training data - [[AI Skill Supply Chain Security]] -- skills as attack vector ## AI Strategy and Future - [[Agentic Era]] -- autonomous AI work - [[AI and Jobs]] -- labor market impact - [[Digital Twin]] -- AI replicas - [[AI Transformation Playbook]] -- enterprise adoption framework - [[AI Implementation Roadmap]] -- phased adoption path - [[AI for Enterprise Leaders]] -- CTO/CIO framing - [[Team AI Onboarding]] -- team adoption playbook - [[Enterprise AI Deployment]] -- rolling out AI at scale - [[AI Organizational Memory]] -- institutional AI memory - [[Artificial General Intelligence (AGI)]] -- human-level AI - [[AI Sustainability]] -- environmental cost of AI - [[Preparing for the future of knowledge work]] -- adapting to AI ## Building with AI (Developer) - [[AI Engineering]] -- building AI-powered systems - [[AI Tool Use]] -- giving AI external tools - [[Model Context Protocol (MCP)]] -- open standard for AI-tool connection - [[Retrieval-Augmented Generation (RAG)]] -- external knowledge retrieval - [[RAG Pipelines]] -- end-to-end retrieval systems - [[Semantic Search]] -- meaning-based search - [[Vector Store]] -- embedding databases - [[AI Observability]] -- monitoring AI in production - [[Model routing]] -- directing to right model by task - [[AI Cost Management]] -- pricing and optimization - [[AI Evaluation]] -- measuring output quality - [[AI Model Selection]] -- choosing the right model - [[Vibe Coding]] -- AI generates, human ships without review - [[Vibe Engineering]] -- AI generates, human reviews everything - [[AI Coding Maturity Levels]] -- progression of AI dev practices - [[AI-Assisted Development Workflow]] -- PRD to testing flow - [[Agentic TDD]] -- test-driven development for agents - [[How coding agents work]] -- internals of coding agents - [[AI and the Shifting Role of Developers]] -- from coders to architects - [[Code is cheap, quality is not]] -- the new bottleneck - [[Unreviewed AI code anti-pattern]] -- shipping without review - [[Prompt Chaining]] -- sequential prompt pipelines ## References - ## Related - [[AI Concepts Teaching Map]] - [[The Context Layer (Own Book)]] - [[Knowledge-Context Pipeline]]