Every term you need,
explained clearly.
Key terms and concepts in prompt engineering, LLM operations, and AI application development — for developers and teams.
Chain-of-Thought Prompting
A prompting technique that instructs the LLM to break down complex problems into intermediate reasoning steps before producing a final answer, significantly improving accuracy on multi-step tasks.
Prompt EngineeringFew-Shot Prompting
A prompting technique where one or more input-output examples are included in the prompt to demonstrate the desired behavior, format, or reasoning pattern for the LLM to follow.
Prompt EngineeringPrompt Blocks
Typed, independently editable sections that compose a structured prompt, where each block has a designated purpose such as role definition, context, instructions, guardrails, or output format.
Prompt EngineeringPrompt Chaining
A technique where multiple prompts are connected in sequence, with the output of one prompt serving as input to the next, enabling complex multi-step workflows that exceed the capability of a single prompt.
Prompt EngineeringPrompt Engineering
The discipline of designing, structuring, and iterating on instructions given to LLMs to elicit accurate, consistent, and useful outputs for specific use cases.
Prompt EngineeringPrompt Optimization
The iterative process of refining prompts to maximize output quality, consistency, and efficiency, typically through systematic testing, evaluation, and data-driven adjustments.
Prompt EngineeringPrompt Template
A reusable prompt structure containing variable placeholders (e.g., {{user_name}}, {{context}}) that are dynamically filled at runtime, enabling the same prompt to serve different inputs and scenarios.
Prompt EngineeringStructured Prompts
Prompts organized into typed, labeled sections or blocks — such as role, context, instructions, guardrails, and output format — rather than written as a single continuous block of text.
Prompt EngineeringSystem Prompt
A special instruction provided to an LLM at the beginning of a conversation that defines its behavior, personality, constraints, and output format for the entire session.
Prompt EngineeringToken Optimization
Techniques for reducing the number of tokens consumed by prompts and responses while maintaining output quality, directly lowering costs and improving response latency in LLM applications.
Prompt EngineeringVariable Interpolation
The process of replacing placeholder tokens (such as {{variable_name}}) in a prompt template with actual runtime values, enabling dynamic prompts that adapt to different users, contexts, and inputs.
Prompt EngineeringZero-Shot Prompting
A prompting approach where the LLM is given only instructions and context without any input-output examples, relying entirely on the model's pre-trained knowledge to perform the task.
Prompt EngineeringDraft/Published Workflow
A two-state prompt lifecycle where prompts exist as editable drafts during development and become immutable published versions when promoted to production, separating work-in-progress from production-ready content.
LLM OpsEnvironment-Scoped Prompts
A deployment strategy where the same prompt identifier serves different versions depending on the requesting environment — development, staging, or production — enabling safe testing without affecting live users.
LLM OpsLLM Evaluation
The systematic process of measuring the quality, accuracy, safety, and reliability of LLM outputs against defined criteria, using automated metrics, human review, or model-based judging.
LLM OpsLLMOps
The set of practices, tools, and workflows for operationalizing large language model applications in production, covering prompt management, evaluation, monitoring, cost control, and reliability.
LLM OpsPrompt A/B Testing
The practice of running two or more prompt variants simultaneously on live traffic to statistically determine which version produces better outcomes against defined metrics.
LLM OpsPrompt Collaboration
The practice of multiple stakeholders — prompt engineers, product managers, domain experts, and developers — working together on prompt development through shared tooling, review workflows, and role-based access.
LLM OpsPrompt Deployment
The process of promoting a tested and approved prompt version from a development or staging state to production, making it available to live applications through an API or delivery mechanism.
LLM OpsPrompt Evaluation
The process of measuring prompt quality against defined criteria such as accuracy, relevance, safety, and format compliance, distinct from broader LLM evaluation by focusing specifically on how well the prompt elicits desired model behavior.
LLM OpsPrompt Lifecycle
The complete set of stages a prompt goes through from initial authoring and iteration, through testing and review, to deployment in production, and ongoing monitoring and refinement.
LLM OpsPrompt Management
The practice of organizing, versioning, testing, and deploying LLM prompts through a centralized platform rather than embedding them directly in application code.
LLM OpsPrompt Registry
A centralized catalog of all prompts within an organization, providing a single source of truth for discovery, access control, and operational visibility across teams and applications.
LLM OpsPrompt Rollback
The ability to revert a production prompt to a previously published version when issues are detected, providing a rapid recovery mechanism that does not require application code changes.
LLM OpsPrompt Testing
The systematic validation of prompt behavior before deployment, using test cases, automated assertions, and evaluation criteria to catch regressions and verify that prompts meet quality standards.
LLM OpsPrompt Versioning
The practice of maintaining a complete history of changes to LLM prompts, enabling teams to compare versions, roll back to previous states, and manage environment-specific deployments.
LLM OpsAI Guardrails
Safety constraints, behavioral boundaries, and policy enforcement mechanisms applied to AI systems to prevent harmful outputs, ensure compliance, and maintain alignment with organizational values.
SecurityGuardrails
Safety constraints and behavioral boundaries embedded in prompts or applied as post-processing layers to prevent LLMs from generating harmful, off-topic, or policy-violating outputs.
SecurityPrompt Governance
The set of policies, controls, and processes that organizations implement to manage prompt changes at scale, ensuring consistency, compliance, and accountability across teams and applications.
SecurityPrompt Injection
A security attack where malicious input is crafted to override or manipulate an LLM's system prompt, causing the model to ignore its instructions and perform unintended actions.
SecurityContext Window
The maximum number of tokens (input plus output) that an LLM can process in a single request, which determines how much information can be included in a prompt and response.
ArchitectureModel-Agnostic Prompts
Prompts designed to produce consistent, high-quality results across different LLM providers and model versions, reducing vendor lock-in and enabling flexible model selection.
ArchitecturePrompt API
A REST or HTTP interface that allows applications to fetch, manage, and deliver prompts programmatically, decoupling prompt content from application code and enabling runtime updates without redeployment.
ArchitecturePrompt Caching
The practice of storing and reusing LLM responses for identical or semantically similar prompt inputs, reducing latency and cost by avoiding redundant model calls.
ArchitecturePrompt Compilation
The process of assembling structured prompt blocks — role, context, instructions, guardrails, output format — into a single prompt string, including ordering, formatting, and variable interpolation.
ArchitectureRetrieval-Augmented Generation (RAG)
An architecture pattern that enhances LLM responses by retrieving relevant documents from an external knowledge base and including them in the prompt as context before generating a response.
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