Technical Glossary
Mechanism-first definitions for commands, architecture patterns, and key concepts across Docker, Kubernetes, Gen AI, and Agentic AI.
Storage driver combining distinct read-only image layers and a read-write layer into a single unified directory tree.
Storage optimization strategy deferring file copies until modifications occur, sharing underlying layer blocks.
Concurrent, DAG-based image builder engine in Docker offering parallel step execution and secret mounting.
Dockerfile design pattern using multiple FROM instructions to separate build-time SDK dependencies from slim runtime artifacts.
Open Container Initiative specification defining container image tarball structures, manifests, and layer blobs.
CLI command creating and starting multi-container services declared in a compose YAML manifest.
HTTP API service storing and serving versioned OCI image manifests and content-addressable layer blobs.
API object managing external HTTP/HTTPS routing rules to cluster-internal Services.
Continuous control loop pattern comparing actual cluster state against declared target state in etcd.
Workload controller managing stateful applications requiring unique network IDs and ordered volume provisioning.
Package manager for Kubernetes bundling YAML templates and default value overrides into versioned Charts.
Primary node agent ensuring containers described in PodSpecs are instantiated and healthy on worker hosts.
Neural network architecture relying entirely on self-attention mechanisms to compute contextual representations in parallel.
Mathematical operation computing context weights by comparing Query, Key, and Value vector dot-products.
Architecture pattern augmenting LLM prompts with relevant context retrieved dynamically from external vector databases.
Hyperparameter scaling logit probabilities before softmax to tune generation randomness and entropy.
Sampling strategy filtering candidate tokens to the smallest set whose cumulative probability reaches threshold P.
Database engine specialized for indexing and performing fast nearest-neighbor similarity searches over vector embeddings.
Control pattern decomposing high-level user tasks into DAG sub-goals executed sequentially or in parallel by subagents.
Agent self-correction loop evaluating tool outputs or generated code against criteria before returning final output.
Open client-server protocol standardizing how AI agents discover and invoke remote tools, resources, and prompts.
State persistence architecture dividing context into short-term working context, long-term vector memory, and episodic logs.