Prompt Caching stores reusable prompt context so repeated requests can skip redundant processing and improve performance.

Token Budget is the practical limit you set for prompt and completion tokens to balance quality, latency, and cost.

A Prompt Template is a standardized prompt format that injects variable content into fixed instructions for consistent outputs.

Related AI terms: Prompt Enhancement and Prompt-to-Code.

Guardrails are technical and procedural constraints, such as filters and validators, that enforce safe and reliable AI behavior.

Related AI terms: Model Card and Content Credentials.

Prompt Injection is an adversarial technique that embeds manipulative instructions in user or retrieved content to subvert model behavior.

First Draft is an AI workflow for quickly generating editable interface concepts. Teams use it to move quickly from idea to wireframe and then refine output with Prompt-to-Code.

Prompt-to-Code describes workflows where AI converts prompts and design intent into code structure. It is commonly paired with Prompt Template systems and custom Code Component output.

Prompt Enhancement is an AI-assisted rewrite step that expands or clarifies user intent before generation. It often works with a Prompt Template and explicit System Prompt constraints.

Classifier-Free Guidance (CFG) is a sampling technique that adjusts prompt adherence versus diversity in generated outputs. It is a common control in Diffusion Model pipelines and complements Prompt Enhancement.

Prompt-to-Prompt Editing changes specific image attributes by adjusting textual instructions while preserving overall scene structure. It is closely related to Prompt Enhancement and iterative Text-to-Image Generation.

The ability of AI models to translate intent—expressed as prose, pseudocode, or examples—into executable source code. Code generation underpins vibe coding, autocomplete tools, and agentic workflows. Generated code should always be reviewed for correctness, security, and maintainability, since models can produce plausible-looking but incorrect implementations.

Writing software by expressing intent in conversational language rather than formal syntax. AI models translate the natural-language description into runnable code. Natural language programming is the conceptual foundation of vibe coding and is enabled by advances in large language models. It dramatically lowers the barrier to entry for non-developers, though it still requires understanding of what good code looks like in order to review AI output effectively.