SECTION 1 — Core LLM & Generative AI (1–200)
A. Model Foundations (1–50)
- Large Language Model (LLM) – Transformer-based model trained on massive text corpora.
- Transformer – Neural architecture using attention mechanisms.
- Self-Attention – Mechanism computing token-to-token relevance.
- Multi-Head Attention – Parallel attention layers capturing diverse relationships.
- Positional Encoding – Injects sequence order into tokens.
- Token – Smallest processed text unit.
- Tokenization – Splitting text into tokens.
- Subword Tokenization – Breaking words into meaningful subunits.
- Byte Pair Encoding (BPE) – Frequency-based token merging algorithm.
- SentencePiece – Language-independent tokenization method.
- Context Window – Maximum tokens processed in one pass.
- Embedding – Dense vector representation of text.
- Embedding Space – High-dimensional semantic vector space.
- Encoder – Processes input sequences into representations.
- Decoder – Generates output tokens sequentially.
- Encoder-Decoder – Architecture for sequence transformation tasks.
- Sequence-to-Sequence – Input-to-output mapping model design.
- Attention Mask – Controls visible tokens during attention.
- Logits – Raw prediction scores before normalization.
- Softmax – Converts logits to probabilities.
- Inference – Generating predictions from trained model.
- Pretraining – Large-scale unsupervised initial training.
- Fine-Tuning – Task-specific model adaptation.
- Instruction Tuning – Training model to follow instructions.
- Reinforcement Learning from Human Feedback (RLHF) – Aligning model via human preferences.
- Supervised Fine-Tuning (SFT) – Fine-tuning on labeled instruction data.
- Knowledge Distillation – Transferring knowledge to smaller model.
- Quantization – Reducing numerical precision for efficiency.
- Pruning – Removing low-importance weights.
- Parameter-Efficient Fine-Tuning (PEFT) – Updating minimal parameters.
- LoRA – Low-rank adaptation fine-tuning method.
- Prefix Tuning – Training prefix vectors instead of full weights.
- Prompt Tuning – Learning soft prompt embeddings.
- Transfer Learning – Reusing pretrained model knowledge.
- Zero-Shot Learning – No examples provided in prompt.
- One-Shot Learning – Single example provided.
- Few-Shot Learning – Few examples provided.
- In-Context Learning – Learning from prompt examples dynamically.
- Chain-of-Thought (CoT) – Step-by-step reasoning prompting.
- Self-Consistency – Sampling multiple reasoning paths and selecting best.
- Tree-of-Thought – Multi-branch reasoning framework.
- Mixture of Experts (MoE) – Sparse expert subnetwork activation.
- Sparse Activation – Activating subset of model parameters.
- Beam Search – Multi-path decoding strategy.
- Greedy Decoding – Selecting highest-probability token.
- Top-k Sampling – Sampling from top-k tokens.
- Top-p Sampling – Sampling from cumulative probability mass.
- Temperature – Controls randomness in output.
- Logit Bias – Adjusting token probabilities manually.
- Stop Sequence – Token pattern that halts generation.
B. Prompting & Output Control (51–100)
- Prompt – Input instruction to model.
- System Prompt – Behavioral instruction defining assistant role.
- User Prompt – User-provided query.
- Assistant Response – Model-generated output.
- Prompt Engineering – Designing effective prompts.
- Prompt Template – Reusable structured prompt format.
- Prompt Chaining – Sequential prompt workflows.
- Context Injection – Adding external data to prompt.
- Output Formatting – Enforcing structured responses.
- Structured Output – Schema-based response (e.g., JSON).
- JSON Mode – Model constrained to valid JSON.
- Tool Calling – Model invoking external APIs.
- Function Calling – Structured tool execution method.
- ReAct Pattern – Interleaving reasoning and acting.
- Hallucination – Confident but incorrect output.
- Grounding – Anchoring output in trusted data.
- Guardrails – Safety enforcement constraints.
- Content Moderation – Filtering harmful outputs.
- Safety Filter – Automated harmful content detection.
- Prompt Injection – Malicious instruction override attack.
- Jailbreak – Circumventing safety constraints.
- Adversarial Prompt – Designed to manipulate output.
- Deterministic Output – Consistent output for same input.
- Log Probabilities – Token-level likelihood scores.
- Streaming Response – Token-by-token output delivery.
- Token Limit – Maximum tokens allowed per request.
- Token Budgeting – Managing token allocation.
- Context Truncation – Removing excess tokens.
- Long-Context Model – Supports extended token windows.
- Latency – Time to generate response.
- Throughput – Requests processed per unit time.
- Cold Start – Delay during first initialization.
- Warm Start – Faster response after initialization.
- Inference Optimization – Performance improvement techniques.
- GPU Acceleration – Hardware-based speed enhancement.
- CPU Inference – Running inference on CPU hardware.
- Distributed Inference – Scaling inference across nodes.
- Batch Inference – Processing multiple inputs together.
- Real-Time Inference – Immediate prediction serving.
- Model Endpoint – Deployed model API interface.
- API Rate Limiting – Restricting request frequency.
- API Throttling – Slowing traffic during load spikes.
- Autoscaling – Dynamic resource scaling.
- Load Balancing – Distributing requests evenly.
- SLA – Service Level Agreement guarantee.
- SLO – Service Level Objective target metric.
- SLI – Service Level Indicator measurement.
- Observability – Monitoring system via logs, metrics, traces.
- Trace Logging – Recording execution flow.
- Production Deployment – Releasing system to live users.
C. RAG & Retrieval Systems (101–200)
- Retrieval-Augmented Generation (RAG) – Combining retrieval with generation.
- Retriever – Fetches relevant documents.
- Generator – Produces final answer.
- Vector – Numeric representation of semantic meaning.
- Vector Similarity – Measuring closeness between vectors.
- Cosine Similarity – Angle-based similarity metric.
- Dot Product – Magnitude-weighted similarity metric.
- Euclidean Distance – Straight-line distance metric.
- Manhattan Distance – Grid-based distance metric.
- Vector Index – Data structure for fast search.
- Approximate Nearest Neighbor (ANN) – Fast approximate similarity search.
- HNSW – Hierarchical navigable small world index algorithm.
- IVF Index – Inverted file index structure.
- FAISS – Vector similarity library by Meta.
- Hybrid Search – Combining keyword + vector search.
- Keyword Search – Lexical matching retrieval.
- BM25 – Probabilistic ranking algorithm.
- Dense Retrieval – Embedding-based retrieval.
- Sparse Retrieval – Keyword-based retrieval.
- Cross-Encoder – Joint query-document scoring model.
- Bi-Encoder – Separate encoding for query and document.
- Re-ranking – Reordering retrieved results.
- Document Parsing – Extracting text from files.
- OCR – Extracting text from images.
- Data Chunking – Splitting documents into parts.
- Chunk Size – Token length per chunk.
- Chunk Overlap – Shared tokens between chunks.
- Metadata Filtering – Filtering results by attributes.
- Knowledge Base – Structured domain data repository.
- Knowledge Graph – Entity-relationship structured graph.
- Query Expansion – Expanding user query semantically.
- Context Compression – Reducing retrieved context size.
- Citation Tracking – Referencing source documents.
- Retrieval Precision – Correct retrieved documents ratio.
- Retrieval Recall – Coverage of relevant documents.
- Retrieval F1 Score – Balanced retrieval metric.
- Ground Truth – Verified correct answer set.
- Embedding Drift – Change in embedding distribution.
- Index Refresh – Updating vector index.
- Retrieval Latency – Time to fetch documents.
- Context Ranking – Ordering document importance.
- Passage Ranking – Ranking document segments.
- Multi-Hop Retrieval – Retrieving across multiple reasoning steps.
- Graph RAG – Retrieval using graph relationships.
- Structured RAG – Retrieval from structured databases.
- Retrieval Cache – Storing previous search results.
- Embedding Cache – Storing computed embeddings.
- Similarity Threshold – Minimum similarity score.
- Top-k Retrieval – Fetching top-k documents.
- Retrieval Confidence – Likelihood retrieval is correct.
- Data Ingestion – Loading documents into system.
- Data Pipeline – Automated processing workflow.
- Index Sharding – Splitting index across nodes.
- Vector Compression – Reducing vector storage size.
- Real-Time Retrieval – Instant search execution.
- Offline Indexing – Batch index building.
- Semantic Search – Meaning-based search.
- Context Window Optimization – Efficient context usage.
- Knowledge Refresh – Updating knowledge base.
- Embedding Model – Model generating embeddings.
- Vector Store – Storage system for embeddings.
- Vector Database – Specialized embedding database.
- Retrieval Evaluation Dataset – Dataset for retrieval testing.
- Query Embedding – Vector of user query.
- Passage Embedding – Vector of document chunk.
- Context Relevance – Relevance of injected content.
- Retrieval Benchmark – Standardized retrieval test.
- Search Recall@k – Recall measured at top-k.
- Search Precision@k – Precision measured at top-k.
- Retrieval Pipeline – End-to-end retrieval workflow.
- Index Replication – Duplicate index for reliability.
- Vector Partitioning – Dividing vectors across clusters.
- Knowledge Integration – Combining multiple data sources.
- Context Grounding Score – Measure of answer grounding.
- Retrieval Monitoring – Tracking retrieval performance.
- Embedding Fine-Tuning – Improving domain embeddings.
- Query Rewriting – Reformulating user query.
- Document Ranking Model – Model scoring relevance.
- Retrieval Scalability – Handling large-scale search.
- Data Freshness – Recency of indexed data.
- Context Diversity – Ensuring varied document retrieval.
- Multi-Vector Retrieval – Multiple embeddings per doc.
- Retrieval Bias – Systematic retrieval imbalance.
- Semantic Chunking – Splitting by meaning boundaries.
- Retrieval Pipeline Latency – End-to-end retrieval time.
- Context Injection Strategy – Method of injecting documents.
- Knowledge Validation – Verifying source reliability.
- Retrieval Cost Optimization – Reducing retrieval compute cost.
- Embedding Dimensionality – Size of embedding vector.
- Index Optimization – Improving search efficiency.
- Query Latency – Time to process query.
- Context Re-ranking – Secondary ranking stage.
- Retrieval Drift – Performance degradation over time.
- Corpus Expansion – Adding new documents.
- Index Versioning – Tracking index changes.
- Semantic Filtering – Filtering by semantic relevance.
- Search API – Endpoint for retrieval queries.
- Vector Similarity Thresholding – Filtering low similarity results.
- Retrieval Audit – Logging retrieval behavior.
- Grounded Answering – Answer strictly supported by retrieved data.
SECTION 2 — Agents, Orchestration, LLMOps & ML Lifecycle (201–350)
A. Agent Engineering (201–250)
- AI Agent – Autonomous LLM-powered system that can reason and act.
- Single-Agent System – One agent handling full task lifecycle.
- Multi-Agent System – Multiple agents collaborating on tasks.
- Agent Loop – Iterative cycle of reasoning, acting, observing.
- Planner – Component that decomposes tasks into steps.
- Executor – Component that performs planned actions.
- Tool Registry – Collection of callable tools available to agent.
- Tool Invocation – Executing an external tool from agent.
- Tool Abstraction – Standardized interface for tool access.
- Structured Tool Output – Tool response formatted in schema.
- Agent Memory – Storage mechanism for past interactions.
- Short-Term Memory – Session-level conversational memory.
- Long-Term Memory – Persistent cross-session memory.
- Episodic Memory – Memory of specific past events.
- Semantic Memory – Knowledge-based memory store.
- Working Memory – Temporary reasoning buffer.
- State Management – Tracking agent state during execution.
- Task Decomposition – Breaking complex tasks into subtasks.
- Autonomous Agent – Agent operating with minimal human input.
- Human-in-the-Loop (HITL) – Human oversight in execution.
- Agent Orchestration – Coordinating multiple agents/tools.
- Task Planning – Designing execution roadmap.
- Reasoning Step – Logical thinking stage in loop.
- Action Step – Tool execution stage.
- Observation Step – Processing tool results.
- Reflection Loop – Self-evaluation before next step.
- Retry Logic – Reattempting failed tool calls.
- Timeout Handling – Managing stalled executions.
- Agent Evaluation – Measuring agent performance.
- Agent Logging – Recording execution traces.
- Agent Monitoring – Tracking runtime behavior.
- Execution Graph – Visual task dependency flow.
- Multi-Step Workflow – Sequential execution pipeline.
- Tool Latency – Time taken by external tool.
- API Integration – Connecting agent to APIs.
- REST API – HTTP-based service interface.
- JSON Schema – Structured data validation format.
- Error Handling – Managing execution failures.
- Tool Permissioning – Restricting tool access.
- Agent Sandbox – Isolated execution environment.
- Conversational Agent – Dialogue-based AI agent.
- Workflow Agent – Task automation-focused agent.
- Event-Driven Agent – Triggered by external events.
- Planner-Executor Pattern – Separation of planning and execution.
- ReAct Agent – Reasoning + acting structured agent.
- Tool-Augmented LLM – LLM enhanced with external tools.
- Delegation Agent – Agent assigning tasks to sub-agents.
- Agent Memory Window – Token limit for memory recall.
- Session State – Runtime conversation context.
- Agent Security Boundary – Isolation of agent capabilities.
B. Workflow & Orchestration (251–300)
- Workflow Orchestration – Coordinating multi-step pipelines.
- DAG (Directed Acyclic Graph) – Task dependency structure.
- Task Dependency – Order relationship between tasks.
- Pipeline – Sequential data or execution flow.
- Orchestration Engine – Executes workflow logic.
- Scheduler – Triggers workflows at defined times.
- Job Queue – Task waiting line for execution.
- Event Trigger – Event-based workflow activation.
- Message Queue – Asynchronous communication system.
- Pub/Sub – Publish-subscribe messaging model.
- Microservices Architecture – Modular independent services.
- Service Mesh – Infrastructure managing service communication.
- API Gateway – Centralized API traffic manager.
- Reverse Proxy – Forwards client requests internally.
- Containerization – Packaging app with dependencies.
- Docker – Container runtime platform.
- Kubernetes – Container orchestration platform.
- Pod – Smallest deployable Kubernetes unit.
- Cluster – Group of compute nodes.
- Infrastructure as Code (IaC) – Provisioning via code.
- Terraform – Infrastructure automation tool.
- Helm – Kubernetes package manager.
- CI/CD – Continuous integration and deployment.
- GitOps – Git-driven infrastructure management.
- Version Control – Managing code revisions.
- Branch Strategy – Git branching model.
- Code Review – Peer review of changes.
- Deployment Pipeline – Automated release workflow.
- Blue-Green Deployment – Dual environment switch release.
- Canary Deployment – Gradual release to subset users.
- Shadow Deployment – Silent testing in production.
- Rollback Strategy – Reverting to stable version.
- Release Management – Coordinating deployments.
- Change Management – Controlling system modifications.
- Observability Stack – Integrated monitoring tools.
- Metrics Store – Storage for performance metrics.
- Logging Framework – Structured log collection system.
- Monitoring Dashboard – Visual system health panel.
- Alert Rule – Threshold-based notification trigger.
- Incident Response – Structured failure handling process.
- Root Cause Analysis – Identifying failure origin.
- SLA Monitoring – Tracking contract compliance.
- Capacity Planning – Forecasting resource needs.
- Load Testing – Simulating heavy traffic.
- Stress Testing – Testing beyond capacity limits.
- Performance Benchmark – Standardized performance measurement.
- Horizontal Scaling – Adding more instances.
- Vertical Scaling – Increasing single instance resources.
- High Availability – Ensuring minimal downtime.
- Fault Tolerance – Continuing despite failures.
C. LLMOps, MLflow & Model Lifecycle (301–350)
- LLMOps – Operational management of LLM systems.
- MLOps – Production lifecycle management for ML models.
- DevOps – Dev + operations collaboration framework.
- Experiment Tracking – Logging runs and metrics.
- Run ID – Unique experiment identifier.
- Parameter Logging – Recording hyperparameters.
- Metric Logging – Recording performance metrics.
- Artifact Logging – Storing model files and outputs.
- Model Registry – Central model storage system.
- Model Version – Numbered model iteration.
- Model Stage – Lifecycle stage (Staging, Production).
- Model Transition – Promoting model between stages.
- Prompt Versioning – Tracking prompt updates.
- Model Lineage – Tracking model origin and changes.
- Data Lineage – Tracking data transformations.
- Feature Store – Centralized feature repository.
- Feature Engineering – Creating predictive variables.
- Feature Extraction – Deriving features from raw data.
- Feature Scaling – Normalizing feature values.
- Drift Detection – Identifying performance degradation.
- Data Drift – Change in input distribution.
- Concept Drift – Change in input-output relationship.
- Model Drift – Performance degradation over time.
- A/B Testing – Comparing model variants.
- Shadow Testing – Evaluating new model silently.
- Evaluation Pipeline – Automated evaluation workflow.
- Offline Evaluation – Testing using static dataset.
- Online Evaluation – Testing in live environment.
- Benchmark Dataset – Standard comparison dataset.
- Cost Tracking – Monitoring compute and API costs.
- Token Tracking – Monitoring token consumption.
- Latency Tracking – Measuring response times.
- Continuous Monitoring – Ongoing performance tracking.
- Continuous Evaluation – Automated model re-evaluation.
- Incident Log – Record of system failures.
- Audit Trail – Logged system activity history.
- Compliance Check – Validating regulatory adherence.
- Governance Workflow – Approval and control process.
- Responsible AI – Ethical AI deployment practices.
- Bias Detection – Identifying unfair model behavior.
- Fairness Metric – Quantifying fairness level.
- Explainability – Interpreting model decisions.
- SHAP – Feature attribution explainability method.
- LIME – Local surrogate explanation method.
- Confidence Score – Probability of prediction reliability.
- Model Validation – Ensuring performance before release.
- Production Readiness – Meeting enterprise deployment standards.
- Rollout Strategy – Planned deployment approach.
- Feedback Loop – Using user data for improvement.
- AI Lifecycle – End-to-end AI system management process.
SECTION 3 — Databricks, Lakehouse, Enterprise Deployment, Security & Governance (351–500)
A. Databricks & Lakehouse Architecture (351–400)
- Lakehouse Architecture – Unified data platform combining data lake and warehouse.
- Delta Lake – Storage layer enabling ACID transactions on data lakes.
- Delta Table – Structured table built on Delta Lake.
- Delta Log – Transaction log maintaining table state.
- ACID Transactions – Atomic, consistent, isolated, durable operations.
- Unity Catalog – Centralized governance layer for data and AI assets.
- Catalog – Top-level data organization container.
- Schema – Logical grouping of tables.
- Workspace – Collaborative development environment.
- Notebook – Interactive coding document.
- Cluster – Compute resources for processing workloads.
- Serverless Compute – Auto-managed scalable compute resources.
- Job – Scheduled or triggered execution task.
- SQL Warehouse – Dedicated compute for SQL workloads.
- Auto Loader – Incremental file ingestion utility.
- Structured Streaming – Scalable stream processing engine.
- Batch Processing – Processing data in fixed intervals.
- Real-Time Processing – Immediate data handling.
- Feature Store – Central repository for ML features.
- Model Serving – Hosting models for inference.
- Model Endpoint – API interface for served models.
- AI Playground – Interactive LLM experimentation interface.
- Vector Search – Managed vector retrieval service.
- Data Lineage Tracking – Monitoring data flow across systems.
- Role-Based Access Control (RBAC) – Permission control via roles.
- Data Masking – Hiding sensitive information.
- PII Handling – Managing personally identifiable data securely.
- Schema Evolution – Adapting schema without breaking queries.
- Z-Ordering – Optimizing data layout for query speed.
- Data Partitioning – Dividing data for performance.
- Query Optimization – Improving SQL execution efficiency.
- Data Validation – Ensuring data correctness.
- Data Quality Rules – Constraints enforcing reliability.
- Data Cleansing – Removing inaccuracies in datasets.
- Data Normalization – Structuring consistent data formats.
- Data Aggregation – Summarizing datasets.
- Structured Data – Organized tabular data.
- Unstructured Data – Raw text, images, etc.
- Semi-Structured Data – JSON/XML-like structured formats.
- Metadata Store – Repository of dataset descriptions.
- Data Catalog – Inventory of data assets.
- Semantic Layer – Business-friendly data abstraction.
- Data API – Programmatic data access interface.
- Data Governance – Policies controlling data usage.
- Data Residency – Geographic data storage control.
- Secure Cluster – Restricted-access compute cluster.
- Cluster Policy – Governance rules for cluster usage.
- Cost Governance – Controlling infrastructure spending.
- Usage Analytics – Monitoring resource consumption.
- Workspace Audit Log – Tracking user activity.
B. Enterprise Deployment & Infrastructure (401–450)
- CI/CD Pipeline – Automated build, test, deploy workflow.
- Infrastructure Monitoring – Tracking system performance.
- Logging System – Centralized log storage.
- Log Aggregation – Consolidating logs across services.
- Monitoring Dashboard – Visual health monitoring interface.
- Alerting System – Automated anomaly notification.
- API Gateway – Central entry point for APIs.
- Reverse Proxy – Routing client traffic internally.
- Microservices Architecture – Modular service-based system design.
- Service Mesh – Manages inter-service communication.
- Container Orchestration – Automated container lifecycle management.
- Edge Deployment – Running services near users.
- Content Delivery Network (CDN) – Distributed content delivery network.
- Serverless Architecture – Event-triggered compute model.
- Event-Driven Architecture – System responding to events.
- Hybrid Cloud – On-prem + cloud integration.
- Multi-Cloud Strategy – Using multiple cloud providers.
- Resource Provisioning – Allocating compute resources.
- Autoscaling Policy – Rules for scaling infrastructure.
- Load Balancer – Traffic distribution component.
- API Monitoring – Tracking API health.
- Rate Limiting – Restricting API request volume.
- Throttling – Slowing excessive traffic.
- Failover Mechanism – Automatic backup activation.
- Disaster Recovery (DR) – Restoring after catastrophic failure.
- Backup Strategy – Scheduled data duplication plan.
- Redundancy – Duplicate components for reliability.
- Cold Start – Startup delay in serverless systems.
- Warm Start – Faster subsequent invocation.
- High Availability (HA) – Minimal downtime system design.
- Reliability Engineering – Designing resilient systems.
- Scalability – Supporting workload growth.
- Capacity Planning – Forecasting infrastructure demand.
- Performance Tuning – Optimizing system speed.
- Cost Optimization – Reducing operational expense.
- Technical Debt – Accumulated engineering shortcuts.
- System Health Check – Automated service validation.
- Incident Management – Coordinated outage response.
- Root Cause Analysis – Identifying underlying issue.
- Deployment Validation – Verifying successful release.
- Change Management – Controlled modification process.
- Release Management – Structured deployment planning.
- Version Control – Managing source code versions.
- Git Workflow – Branch-based collaboration strategy.
- Integration Testing – Testing combined components.
- System Testing – Testing full system functionality.
- Regression Testing – Ensuring updates don’t break features.
- Smoke Testing – Basic health verification test.
- Performance Testing – Evaluating response under load.
- Security Audit – Reviewing security posture.
C. Security, Compliance & AI Governance (451–500)
- Identity and Access Management (IAM) – Managing user identities and permissions.
- Authentication – Verifying user identity.
- Authorization – Granting access rights.
- OAuth – Secure delegated authorization protocol.
- API Key – Token granting API access.
- Secret Management – Secure credential storage.
- Encryption at Rest – Protecting stored data.
- Encryption in Transit – Protecting data during transfer.
- Key Management System (KMS) – Secure encryption key lifecycle.
- Multi-Factor Authentication (MFA) – Multiple identity checks.
- Single Sign-On (SSO) – Unified authentication across services.
- Firewall – Network traffic filtering system.
- Network Security – Protecting infrastructure communication.
- Secure Token – Encrypted authentication token.
- IP Whitelisting – Allowing access from approved IPs.
- Data Exfiltration – Unauthorized data transfer.
- Threat Modeling – Identifying potential security risks.
- Vulnerability Assessment – Identifying system weaknesses.
- Penetration Testing – Simulated attack evaluation.
- Compliance Framework – Regulatory requirement structure.
- GDPR – European data protection regulation.
- SOC 2 – Security compliance certification standard.
- HIPAA – U.S. healthcare data protection law.
- Audit Trail – Record of system/user actions.
- Governance Policy – Organizational AI usage rules.
- Responsible AI – Ethical AI development practice.
- AI Risk Management – Identifying and mitigating AI risks.
- Bias Monitoring – Tracking fairness issues.
- Fairness Metric – Quantitative fairness measurement.
- Explainable AI (XAI) – Techniques improving transparency.
- Model Card – Documentation describing model use and risk.
- Risk Register – Log of identified risks.
- Compliance Audit – Regulatory adherence review.
- Data Protection Impact Assessment (DPIA) – Risk evaluation for data systems.
- Secure Deployment – Security-hardened production release.
- Policy Enforcement – Automated rule application.
- Access Logging – Recording system access attempts.
- Governance Workflow – Approval pipeline for AI systems.
- AI Governance Board – Oversight committee for AI usage.
- Production Checklist – Pre-release validation list.
- SLA Breach – Violation of service agreement.
- Risk Mitigation – Reducing identified threats.
- Security Posture – Overall defensive strength.
- Secure Networking – Protected internal communication.
- Data Lifecycle Management – Managing data from creation to deletion.
- Model Lifecycle Management – Managing model from training to retirement.
- System Lifecycle – Managing infrastructure lifespan.
- Enterprise AI Governance – Structured AI oversight at scale.
- Production Monitoring – Live system performance tracking.
- Enterprise Deployment – Large-scale AI system rollout.
SECTION 4 — Advanced Enterprise Architecture, Scalability, Strategy & Future AI (501–650)
A. Advanced Model & Optimization Concepts (501–540)
- Model Compression – Reducing model size while preserving performance.
- Knowledge Transfer – Moving learned patterns between models.
- Domain Adaptation – Adapting model to new domain data.
- Continual Learning – Incrementally learning without forgetting.
- Catastrophic Forgetting – Losing previous knowledge during retraining.
- Curriculum Learning – Training with progressively complex data.
- Synthetic Data – Artificially generated training data.
- Data Augmentation – Expanding training data via transformations.
- Self-Supervised Learning – Learning without explicit labels.
- Contrastive Learning – Learning via similarity comparisons.
- Representation Learning – Learning useful feature representations.
- Embedding Alignment – Aligning embeddings across models.
- Vector Quantization – Compressing embeddings into discrete codes.
- Low-Precision Inference – Running inference with reduced bit precision.
- Model Parallelism – Splitting model across multiple devices.
- Data Parallelism – Splitting data across devices.
- Pipeline Parallelism – Layer-wise distributed execution.
- Gradient Checkpointing – Reducing memory during training.
- Parameter Sharding – Splitting model parameters across nodes.
- Federated Learning – Training across decentralized data sources.
- Edge AI – Running models on edge devices.
- On-Device Inference – Local inference without cloud.
- Distilled Model – Smaller model trained from larger teacher.
- Instruction Dataset – Dataset for instruction tuning.
- Alignment Tuning – Improving model ethical behavior.
- Safety Alignment – Ensuring safe output behavior.
- Preference Modeling – Modeling human preference signals.
- Reinforcement Learning – Learning via reward signals.
- Reward Model – Model estimating quality of outputs.
- Policy Optimization – Updating model via reward feedback.
- Retrieval Fine-Tuning – Improving retriever performance.
- Long-Context Training – Training for large token windows.
- Memory-Augmented Model – Model integrated with external memory.
- Multimodal Model – Model handling text, image, audio.
- Vision-Language Model – Model combining image and text reasoning.
- Audio-Text Model – Model combining speech and language.
- Cross-Modal Retrieval – Searching across modalities.
- Latent Space – Internal compressed representation space.
- Token Embedding – Numeric representation of token meaning.
- Positional Embedding – Encoding token order information.
B. Enterprise Architecture & Scalability Patterns (541–580)
- Enterprise Architecture – Blueprint of organizational systems.
- Reference Architecture – Standardized architectural template.
- Solution Architecture – Design for specific implementation.
- Technical Architecture – Infrastructure-level system design.
- Data Architecture – Structure of data systems.
- AI Architecture – Design of AI system components.
- End-to-End Architecture – Complete system flow design.
- Layered Architecture – Separation of concerns by layers.
- Event Streaming – Continuous real-time data streams.
- API Integration Layer – Middleware connecting services.
- Abstraction Layer – Simplified interface over complexity.
- Service-Level Isolation – Separating service failures.
- Multi-Tenancy – Serving multiple clients in one system.
- Tenant Isolation – Preventing cross-tenant data leakage.
- Resource Isolation – Dedicated compute boundaries.
- Horizontal Partitioning – Splitting data across nodes.
- Vertical Partitioning – Splitting data by function.
- Data Replication – Copying data for reliability.
- Geo-Replication – Replicating across regions.
- Global Load Balancing – Distributing traffic globally.
- Edge Caching – Caching responses near users.
- API Contract – Defined request-response schema.
- Backward Compatibility – Supporting older integrations.
- Forward Compatibility – Supporting future extensions.
- System Interoperability – Seamless system communication.
- Integration Layer – Middleware coordination layer.
- Observability Engineering – Designing measurable systems.
- Chaos Engineering – Testing system resilience intentionally.
- Resilience Pattern – Design for fault resistance.
- Circuit Breaker – Preventing cascading failures.
- Bulkhead Pattern – Isolating failure domains.
- Retry Pattern – Reattempting failed operations.
- Idempotency – Safe repeated execution property.
- Eventual Consistency – Delayed data consistency model.
- Strong Consistency – Immediate consistency guarantee.
- Distributed System – Multi-node coordinated system.
- CAP Theorem – Tradeoff between consistency, availability, partition tolerance.
- Latency Optimization – Minimizing response time.
- Throughput Optimization – Maximizing request capacity.
- Cost-Aware Architecture – Designing with cost constraints.
C. Governance, Risk & Enterprise Strategy (581–620)
- AI Strategy – Organizational AI adoption roadmap.
- Data Strategy – Long-term data utilization plan.
- Digital Transformation – Modernizing systems via technology.
- Stakeholder Alignment – Coordinating business and tech goals.
- Roadmap Planning – Sequenced capability planning.
- Risk Governance – Oversight of organizational risks.
- Model Risk Management – Managing model-related risks.
- Regulatory Compliance – Adhering to legal requirements.
- AI Regulation – Laws governing AI systems.
- Ethical AI – Fair and responsible AI principles.
- Transparency Framework – Disclosure of AI behavior.
- Accountability Framework – Clear responsibility structure.
- Explainability Report – Document describing model logic.
- Audit Readiness – Preparedness for compliance audit.
- Data Classification – Categorizing data sensitivity.
- Access Governance – Controlling system permissions.
- Security Governance – Oversight of cybersecurity controls.
- Third-Party Risk – Risk from external vendors.
- Vendor Assessment – Evaluating external provider security.
- Enterprise Risk Register – Centralized risk documentation.
- AI Approval Workflow – Formal model approval process.
- Model Retirement – Decommissioning outdated models.
- Data Retention Policy – Rules for storing data duration.
- Privacy-by-Design – Embedding privacy into system design.
- Zero Trust Architecture – Never trust, always verify model.
- Incident Escalation – Structured issue reporting hierarchy.
- Business Continuity Plan – Maintaining operations during disruption.
- Operational Resilience – Ability to withstand shocks.
- Governance Automation – Automating policy enforcement.
- Continuous Compliance – Ongoing regulatory validation.
- Audit Logging – Persistent compliance event recording.
- Enterprise Security Architecture – Organization-wide security design.
- Security Hardening – Strengthening system defenses.
- Risk Assessment Matrix – Structured risk scoring.
- Ethical Review Board – Committee reviewing AI ethics.
- Bias Audit – Evaluating fairness issues.
- Transparency Dashboard – Public AI behavior reporting.
- Responsible Deployment – Ethical release strategy.
- AI Ethics Framework – Formal ethical AI guidelines.
- Trustworthy AI – AI that is safe, fair, robust.
D. Future-Ready & Strategic AI Capabilities (621–650)
- Autonomous Systems – Self-operating AI systems.
- Agent Ecosystem – Network of collaborative agents.
- Cognitive Architecture – Framework modeling intelligent behavior.
- Self-Improving AI – AI that iteratively refines itself.
- Meta-Learning – Learning how to learn.
- Adaptive Systems – Systems adjusting to new conditions.
- Real-Time Personalization – Dynamic content customization.
- Context-Aware Computing – Using situational awareness in processing.
- Enterprise Knowledge Fabric – Unified organizational knowledge layer.
- Digital Twin – Virtual model of physical system.
- Intelligent Automation – AI-driven process automation.
- Process Mining – Discovering process patterns from logs.
- Decision Intelligence – AI-assisted decision frameworks.
- Predictive Analytics – Forecasting future outcomes.
- Prescriptive Analytics – Recommending actions.
- Cognitive Search – AI-powered semantic enterprise search.
- Generative Analytics – LLM-generated business insights.
- AI Copilot – Contextual AI assistant for workflows.
- Workflow Automation AI – AI managing enterprise workflows.
- Enterprise AI Platform – Centralized AI development ecosystem.
- Model Marketplace – Internal reusable model repository.
- Prompt Marketplace – Reusable enterprise prompt library.
- AI Center of Excellence – Dedicated AI governance team.
- AI Maturity Model – Measuring AI capability progression.
- Innovation Pipeline – Structured experimentation framework.
- Continuous Innovation – Ongoing capability improvement.
- AI Talent Strategy – Workforce AI skill planning.
- Enterprise AI Roadmap – Multi-year AI implementation plan.
- AI Value Realization – Measuring AI business impact.
- Sustainable AI – Environmentally efficient AI systems.
Enterprise Search & Azure
- Azure AI Search – Managed enterprise search and vector retrieval platform.
- Cognitive Search – AI-enriched search using NLP and indexing pipelines.
- Search Index – Structured searchable data structure.
- Search Indexer – Service that ingests and processes data into an index.
- Skillset – AI enrichment pipeline in Azure Search.
- Cognitive Skills – Prebuilt AI skills for document enrichment.
- Custom Skill – User-defined enrichment function.
- Hybrid Search – Combining keyword and vector search.
- Semantic Ranker – AI ranking model for improved relevance.
- Search Explorer – Tool for testing search queries.
- Vector Field – Index field storing embeddings.
- Azure OpenAI Integration – Direct integration with OpenAI models.
- Managed Identity – Secure authentication between Azure services.
- Role-Based Access Control (RBAC) – Permission management system.
- Private Endpoint – Secure network access to Azure resources.
- Data Source Connector – Integration with external storage.
- Index Schema – Structure definition of searchable fields.
- Relevance Scoring Profile – Custom ranking logic configuration.
- Faceted Navigation – Filtering results by categories.
- Semantic Answers – AI-generated direct answers from documents.