Glossary · C
Terms starting with C
21 of 250 glossary terms. Each entry links to its own page with why it matters and where it fits.
Definitions here are written for engineers, not grammarians: what the term means, why it matters when it comes up, and where it sits in the curriculum — most term pages name the lessons that lean on the concept, so you can jump from a definition straight to the teaching. Use the letter bar below to move through the alphabet, or press the slash key to search the whole corpus directly.
Every entry carries three things besides the definition: the plain aliases it travels under (listed as “also”), a category tag for which stretch of the curriculum it belongs to, and the cross-references that place it next to its neighbors — the terms you would meet in the same lesson. A term earns a page here only when a lesson in the curriculum actually uses it, so the corpus stays the shape of the material instead of the shape of a dictionary.
One honest way to use this page: read down the list of entries below and note every term you could not define in a sentence if a colleague asked. That list — not the whole letter — is your revision list, and each entry links to the reading that closes the gap.
CNN (Convolutional Neural Network)
Models & inference
A neural network that uses convolution operations (sliding filters over the input) to detect local patterns. Stacking convolutions detects increasingly complex features: edges, textures, objects.
CUDA
Models & inference
NVIDIA's platform and programming model for general-purpose computation on compatible GPUs. Deep-learning frameworks use CUDA libraries and kernels to execute many tensor operations in parallel.
Calibration
Evaluation & safety
The agreement between a system's stated confidence and the observed frequency with which predictions at that confidence are correct.
Canary Release
Reliability & operations
A deployment strategy that exposes a new version to a limited slice of traffic or infrastructure before expanding the rollout.
Chain of Thought (CoT)
Prompting & context
Intermediate reasoning used to decompose a task before producing an answer. A prompt can request a visible rationale, while some systems use internal reasoning that is not returned to the user.
Checkpoint
Agents & tools
A durable snapshot used to resume from a known boundary. In a workflow, it stores operational state and artifact references. In model training, it can store parameters, optimizer state, scheduler state, and the training position.
Chunked Prefill
Infrastructure & serving
A serving technique that divides a long prompt's prefill work into smaller schedulable pieces so prompt processing can interleave with decode work from other requests.
Chunking
Retrieval & generation
Dividing source material into retrievable units before indexing. Chunk boundaries, overlap, metadata, and document structure determine whether retrieval returns enough context without flooding the prompt.
Circuit Breaker
AI-native development
A reliability control that temporarily stops calls to a dependency after failures cross a threshold, then probes whether the dependency has recovered.
Coding Agent
AI-native development
An agent specialized for software work that can inspect a repository, edit files, run development tools, and use their outputs to advance a scoped engineering task.
Compensating Action
Agents & tools
A deliberate operation that semantically counteracts a completed side effect when the original operation cannot be rolled back atomically.
Content Provenance
Security & governance
Verifiable information about the origin and editing history of a piece of media or other digital content, including the actors, tools, transformations, and assertions attached to it.
Context Compression
Prompting & context
Reducing the token footprint of source material while attempting to preserve the information required for a later model decision.
Context Engineering
Prompting & context
Designing the full information environment supplied to a model at each step, including instructions, selected files, retrieved evidence, tool results, examples, state, and output constraints.
Context Window
Prompting & context
The maximum token capacity available to one model inference under a specific model and API contract. The capacity may include system instructions, messages, retrieved content, tool exchanges, and generated output, with provider-specific accounting and output limits.
Continuous Batching
Infrastructure & serving
A serving scheduler that adds and removes generation requests at iteration boundaries instead of waiting for every request in a fixed batch to finish.
Contrastive Learning
Math & training
Training by pulling similar pairs closer and pushing dissimilar pairs apart in embedding space. CLIP uses this: matching image-text pairs vs non-matching ones.
Cosine Similarity
Data & representations
The normalized dot product of two vectors. It compares their direction rather than their magnitude and ranges from -1 to 1 for real-valued vectors.
Cost per Successful Task
AI-native development
Total system cost divided by the number of tasks that satisfy a defined success criterion, including retries, failed runs, tool use, and evaluation overhead.
Cross-Attention
Multimodal systems
Attention in which the query representation comes from one sequence or representation while keys and values come from another.
Cross-Entropy
Math & training
A loss based on the negative log probability assigned to the target outcome. In next-token training, it penalizes the model when it assigns low probability to the observed next token.