100 Prompt Engineering Terms (2026)

Prompting a model to perform a task without providing any examples or prior demonstrations; the model relies entirely on its training and the task description.

“Using zero-shot, I asked the model to summarize a legal document with no examples, and it still captured the key clauses.”

Origin: Term from machine learning circa 2009; adopted in prompt engineering by 2022 to distinguish from few-shot methods.

Providing a single example in a prompt to guide the model toward the desired output format or behavior.

“I gave the model one-shot learning with a single customer complaint and its resolution, and it then handled new complaints in the same style.”

Origin: Emerged from few-shot learning research; formalized in prompt engineering literature by 2023.

Including a small number of examples in a prompt to demonstrate the desired task behavior, allowing the model to infer patterns without retraining.

“With few-shot prompting, I provided three examples of product descriptions and the model generated new ones matching the tone and structure.”

Origin: Coined in meta-learning research; became central to prompt engineering after GPT-3 demonstrated its effectiveness in 2020.

A prompting technique that instructs the model to break down reasoning into intermediate steps, improving accuracy on complex tasks like math and logic.

“Instead of asking for the answer directly, I used chain-of-thought to make the model show its work: 'Think step by step, then give your final answer.'”

Origin: Published by Wei et al. in 2022; became one of the most widely adopted prompt engineering techniques.

An extension of chain-of-thought that explores multiple reasoning paths simultaneously, allowing the model to backtrack and evaluate different branches.

“Tree-of-thought let me explore whether to solve the logic puzzle via algebra or graphing, then the model ranked both approaches for validity.”

Origin: Introduced by Yao et al. in 2023 as an evolution of chain-of-thought reasoning.

A decoding method that generates multiple reasoning paths for a single prompt and aggregates them to select the most consistent answer.

“Using self-consistency, I generated ten different chain-of-thought solutions and took a majority vote, which eliminated the model's occasional errors.”

Origin: Proposed by Wang et al. in 2022 as a post-hoc improvement to chain-of-thought reasoning.

A prompt that assigns the model a specific persona or role, such as expert, teacher, or critic, to shape its tone and depth of response.

“I started with a role prompt: 'You are a senior software architect,' and the model immediately gave more sophisticated system design advice.”

Origin: Emerged from early GPT-3 experiments; formalized in prompt engineering best practices by 2023.

A high-level instruction given to the model at the start of a conversation that defines its behavior, values, and constraints across all subsequent turns.

“Our system prompt told the model to always cite sources and decline requests outside its domain, ensuring consistent safety across all user interactions.”

Origin: Formalized in OpenAI's API documentation; became standard practice in production LLM applications by 2023.

The direct instruction or question provided by an end user or application to the model within a conversation.

“When the user prompt asked for a recipe, the model generated a detailed list with ingredients and step-by-step instructions.”

Origin: Standard terminology in conversational AI; used consistently since the adoption of chat-based interfaces.

The model's response or the prefilled text that guides the model toward a specific output format or continuation style.

“I used an assistant prompt that started with 'Here is a detailed analysis:' to make the model produce longer, more structured responses.”

Origin: Emerged from few-shot prompting practices; formalized in multi-turn conversation design.

A prompt that instructs the model to generate or optimize other prompts, enabling automated prompt engineering and iterative refinement.

“I wrote a meta-prompt that asked the model to generate better prompts for customer service, and it produced templates I hadn't considered.”

Origin: Emerged in 2024 as researchers explored prompt optimization at scale.

A reusable prompt structure with placeholders for variables, allowing consistent formatting and easy scaling across multiple tasks or inputs.

“Our prompt template for customer support had slots for customer name, issue type, and context, making responses consistent and personalized.”

Origin: Standard software engineering practice adapted to prompt engineering by 2022.

The model's ability to learn task behavior from examples provided in the prompt context rather than from fine-tuning or retraining.

“Through in-context learning with three examples of email responses, the model adapted its tone to match our brand voice without any model updates.”

Origin: Discovered and formalized by Brown et al. with GPT-3 in 2020; central concept in modern prompt engineering.

A training approach that uses a set of principles or rules to guide model behavior, ensuring outputs align with defined values without extensive human feedback.

“Using constitutional AI principles, the model learned to refuse harmful requests based on a set of constitutional rules rather than individual human annotations.”

Origin: Introduced by Anthropic in 2023 as a scalable safety technique.

Constraints, rules, or filters applied to a model's outputs to prevent harmful, biased, or off-policy responses.

“We implemented guardrails that blocked the model from generating content about weapons or hate speech, even if explicitly prompted.”

Origin: Borrowed from engineering safety terminology; applied to AI systems by 2022.

A prompt or technique designed to circumvent a model's safety guardrails or policies to elicit restricted outputs.

“The researcher tested a jailbreak prompt that tried to trick the model into ignoring its safety guidelines, but the guardrails held.”

Origin: Term from computer security; adapted to AI safety discourse by 2022.

Acronym for 'Do Anything Now'; a jailbreak prompt that attempts to reframe the model as an unrestricted entity willing to bypass safety constraints.

“The DAN prompt tried to convince the model it was in a special mode with no restrictions, but modern models recognize and reject such framings.”

Origin: Emerged in early 2023 as a popular jailbreak technique; now widely mitigated.

An attack where malicious instructions are inserted into user input or data to override the model's intended behavior or system prompt.

“A prompt injection attack embedded hidden instructions in a customer email, trying to make the support bot reveal confidential information.”

Origin: Identified and named by Greshake et al. in 2022; now recognized as a major LLM security risk.

An attack or unintended disclosure where the model reveals or reproduces parts of its system prompt or hidden instructions to a user.

“A prompt leaking vulnerability let users extract the system prompt by asking the model to repeat its instructions, exposing our proprietary guidelines.”

Origin: Identified in early 2023 as models became more transparent about their internal prompts.

Connecting multiple prompts in sequence, where the output of one prompt serves as input to the next, enabling complex multi-step workflows.

“Using prompt chaining, I first extracted key facts, then summarized them, then translated to Spanish, each step feeding the next.”

Origin: Emerged as a practical technique in early LLM applications; formalized by 2023.

A prompting framework that alternates between reasoning and action, allowing models to think through problems and interact with tools or environments.

“Using ReAct, the model reasoned about what information it needed, then called a search tool, then reasoned about the results.”

Origin: Introduced by Yao et al. in 2022 as a framework for interactive problem-solving.

Acronym for Automatic Reasoning and Tool-use; a prompting method that combines chain-of-thought reasoning with dynamic tool selection and execution.

“ART let the model decide when to use a calculator, search tool, or reasoning step based on the problem at hand.”

Origin: Introduced by Paranjape et al. in 2023 as an extension of ReAct.

A framework where the model reflects on its past outputs, identifies errors, and improves subsequent attempts through self-critique and iteration.

“Using Reflexion, when the model's first attempt at code failed, it analyzed the error, reflected on what went wrong, and generated corrected code.”

Origin: Introduced by Shinn et al. in 2023 as a method for iterative improvement.

A technique where the model generates an initial output, then evaluates and improves it based on feedback or self-generated critique.

“With self-refine, the model wrote an essay, then critiqued its own arguments and rewrote sections to strengthen weak points.”

Origin: Emerged in 2023 as researchers explored iterative self-improvement methods.

A framework that uses an external critic or verification mechanism to evaluate model outputs and trigger corrections or refinements.

“CRITIC checked the model's mathematical claims against a calculator, and when they didn't match, it prompted the model to recalculate.”

Origin: Introduced by Gao et al. in 2023 as a verification and correction framework.

Acronym for Program-Aided Language models; a technique where the model generates code or programs to solve problems rather than reasoning directly.

“Using PAL, instead of doing math in text, the model wrote Python code to solve the problem and executed it for the correct answer.”

Origin: Introduced by Gao et al. in 2022 as a method for improving mathematical reasoning.

A designated space in a prompt where the model can write intermediate notes, calculations, or reasoning before producing a final answer.

“I added a scratchpad section where the model could work through the math before giving the final answer, improving accuracy.”

Origin: Emerged as a practical prompting technique; formalized in prompt engineering best practices by 2023.

A framework or tool for systematically testing and scoring model outputs against a set of benchmarks or criteria.

“Our eval harness ran 500 test prompts through the model and scored accuracy, safety, and coherence automatically.”

Origin: Standard software testing terminology adapted to LLM evaluation by 2022.

Acronym for Retrieval-Augmented Generation; a technique that retrieves relevant documents or data before generating responses, improving accuracy and grounding.

“Using RAG, the model first searched a document database for relevant information, then generated an answer grounded in those results.”

Origin: Introduced by Lewis et al. in 2020; became standard for grounded generation by 2023.

A retrieval method combining keyword-based search with semantic vector search to find the most relevant documents from multiple angles.

“Hybrid search found documents both by matching keywords and by semantic similarity, catching results that keyword-only search would miss.”

Origin: Emerged as a best practice in RAG systems by 2024.

A database optimized for storing and retrieving high-dimensional embeddings, enabling fast semantic search for RAG and similarity operations.

“We stored all customer documents as vector embeddings in a vector store, allowing the model to find relevant context in milliseconds.”

Origin: Emerged with the adoption of embeddings for semantic search; formalized in RAG systems by 2023.

A secondary ranking model that reorders search results to improve relevance, often used in RAG pipelines to refine initial retrieval.

“The retrieval step returned 50 documents, and a re-ranker reordered them by relevance, putting the top 5 most useful documents first.”

Origin: Emerged as a refinement technique in information retrieval; adopted in RAG systems by 2023.

Anchoring model outputs to factual sources, data, or verified information to reduce hallucinations and improve truthfulness.

“By grounding responses in retrieved documents, the model could cite specific sources and avoid making up facts.”

Origin: Emerged as a key concern in 2022; formalized in RAG and fact-checking frameworks by 2023.

Including explicit references to source documents or data in model outputs, allowing users to verify claims and trace information provenance.

“The model generated an answer with citations like '[Document 3, paragraph 2]', letting users check the original source.”

Origin: Emerged as a best practice for trustworthy AI by 2023.

A token or string that signals the model to stop generating, used to control output length and prevent unwanted continuations.

“I set a stop sequence of '\n\n' to make the model stop after two line breaks, preventing it from rambling.”

Origin: Standard API feature in language models; widely used since GPT-2.

A parameter controlling randomness in model outputs, where lower values produce deterministic responses and higher values increase diversity.

“I set temperature to 0.2 for factual tasks like summarization, and raised it to 0.9 for creative writing to get more varied ideas.”

Origin: Derived from statistical mechanics; standard in neural network sampling since early deep learning.

A sampling method that selects from the smallest set of tokens whose cumulative probability exceeds a threshold, balancing diversity and coherence.

“Using top-p of 0.9, the model considered tokens accounting for 90% of probability mass, filtering out unlikely but weird options.”

Origin: Introduced by Holtzman et al. in 2019; became standard in language model APIs.

A sampling method that restricts the model to choosing from only the k most likely next tokens, reducing nonsensical outputs.

“With top-k of 40, the model only considered the 40 most probable tokens at each step, avoiding rare and odd continuations.”

Origin: Standard sampling technique in neural language models; widely used since GPT-2.

A parameter that reduces the probability of tokens based on how often they've already appeared in the generated text, encouraging lexical diversity.

“With frequency penalty of 1.0, if the model had already used 'important' three times, it would avoid using it again.”

Origin: Standard feature in OpenAI's API; emerged as a way to reduce repetition.

A parameter that reduces the probability of any token that has appeared at least once in the generated text, discouraging repetition more aggressively than frequency penalty.

“Using presence penalty of 0.6 made the model avoid repeating any word it had already used, even once.”

Origin: Introduced in OpenAI's API; designed to encourage diverse vocabulary.

Log probabilities of tokens; the natural logarithm of the probability assigned by the model to each generated token, useful for confidence scoring.

“By examining logprobs, I could see which tokens the model was uncertain about and flag low-confidence sections of the output.”

Origin: Standard statistical measure; used in language models since inception.

Returning model outputs token-by-token in real-time rather than waiting for the entire response, improving perceived latency and user experience.

“With streaming enabled, users saw the model's response appear word-by-word instead of waiting 3 seconds for the full answer.”

Origin: Became practical with modern APIs; standard feature by 2023.

A capability allowing the model to request execution of external functions or APIs by generating structured function calls, enabling tool integration.

“The model generated a function call like get_weather(city='Boston') and the system executed it, returning current conditions.”

Origin: Introduced by OpenAI in 2023; rapidly adopted as a way to ground models in real tools.

The model's ability to invoke external tools, APIs, calculators, or databases to augment its capabilities and access real-time information.

“With tool use enabled, the model could call a calculator for math, a search API for current facts, and a database for company data.”

Origin: Emerged as a key capability in 2022; formalized in frameworks like ReAct by 2023.

A model output mode that constrains responses to valid JSON format, ensuring structured, parseable data for downstream systems.

“Using JSON mode, the model was guaranteed to output valid JSON even if the content was complex, preventing parsing errors.”

Origin: Introduced by OpenAI in 2023 as a reliability feature.

Constraining model outputs to conform to a predefined data structure or schema, guaranteeing compatibility with downstream systems.

“With schema enforcement, the model's output was validated against a JSON schema before delivery, ensuring all required fields were present.”

Origin: Emerged as a robustness technique in 2023.

A control flow where the model reasons about a task, takes actions, observes results, and repeats until reaching a goal.

“In the agent loop, the model decided to search for information, received results, decided more information was needed, searched again, then provided the answer.”

Origin: Formalized in frameworks like ReAct by 2022; became standard in autonomous AI systems.

An architecture where one model or prompt plans steps to solve a problem and another executes them, separating high-level strategy from low-level action.

“The planner generated a step-by-step strategy for the project, then the executor implemented each step with detailed execution.”

Origin: Emerged as a design pattern in autonomous systems by 2023.

A numerical vector representation of text that captures semantic meaning, used for similarity search, clustering, and retrieval.

“I converted all product descriptions into embeddings and used them to find semantically similar products for recommendations.”

Origin: Foundational technique in NLP; widely adopted for semantic search by 2022.

Finding documents or data based on meaning rather than keyword matching, using embeddings to measure semantic similarity.

“Semantic search found documents about 'vehicle accidents' when searching for 'car crashes', even though the exact words didn't match.”

Origin: Emerged as embeddings became practical; formalized in RAG systems by 2023.

When a model generates plausible-sounding but false or fabricated information, often confidently stating invented facts.

“The model hallucinated a citation to a paper that doesn't exist, complete with a fake author name and publication date.”

Origin: Term adopted from psychology; applied to LLMs by 2021.

Updating a pretrained model's weights on task-specific data to improve performance on specialized domains or behaviors.

“We fine-tuned the base model on medical texts to make it more accurate for healthcare documentation.”

Origin: Standard technique in machine learning; applied to language models since BERT and GPT.

Fine-tuning a model on a diverse set of instruction-response pairs to improve its ability to follow user instructions.

“Instruction tuning on thousands of diverse tasks made the model much better at understanding what users wanted.”

Origin: Introduced by Wei et al. in 2021; became standard for improving model usability.

Acronym for Reinforcement Learning from Human Feedback; a training method where human preferences guide model behavior through reward signals.

“RLHF made the model prefer helpful, harmless, and honest responses by training it to maximize human-rated rewards.”

Origin: Introduced by Christiano et al. in 2017; became central to modern LLM alignment by 2022.

A model trained to score or rank responses based on human preferences, used to provide training signals in RLHF.

“The reward model learned to score responses as good or bad based on human feedback, then guided the main model toward better outputs.”

Origin: Introduced in RLHF research by Christiano et al.; formalized in modern LLM training.

The process of training models to behave in accordance with human values and intentions, reducing harmful or misaligned outputs.

“Alignment training made the model refuse to help with illegal activities and prefer truthfulness over confident-sounding falsehoods.”

Origin: Emerged as a core concern in AI safety research; formalized by 2022.

The maximum amount of text the model can consider at once, measured in tokens; larger windows allow more context and longer conversations.

“With a 128k token context window, the model could analyze an entire book in one prompt without losing earlier information.”

Origin: Fundamental architectural property of transformers; became a key differentiator by 2023.

The basic unit of text processed by language models, usually representing a word or subword; roughly 1 token per 4 characters.

“The phrase 'hello world' is about 2 tokens, but 'tokenization' might be 2 or 3 depending on the model's vocabulary.”

Origin: Standard terminology in NLP; formalized in transformer models.

The process of breaking text into tokens, typically using a vocabulary of subwords that balance coverage and granularity.

“Tokenization converted 'unbelievable' into tokens like 'un', 'be', 'lievable' depending on the tokenizer's vocabulary.”

Origin: Foundational NLP technique; formalized in modern language models.

Sending multiple prompts to the model simultaneously or in groups to improve throughput and reduce per-request latency.

“Using batch processing, I sent 1000 summarization requests at once instead of one-by-one, reducing total time from hours to minutes.”

Origin: Standard technique in distributed computing; applied to LLM APIs by 2023.

Systematically improving prompts through testing, iteration, and refinement to maximize model performance on a task.

“Through prompt optimization, I tested 20 variations and found that adding 'step-by-step' improved accuracy from 78% to 92%.”

Origin: Emerged as an engineering discipline by 2023.

The ability of a prompt to produce consistent, high-quality outputs across different inputs, models, and conditions.

“A robust prompt worked well whether the user provided short or long inputs, formal or casual language.”

Origin: Emerged as a testing concern by 2023.

Combining few-shot learning with chain-of-thought reasoning by providing examples of step-by-step solutions.

“Few-shot chain-of-thought showed the model three examples of multi-step reasoning, dramatically improving its ability to solve new problems.”

Origin: Emerged from combining two techniques by 2022.

Adjusting prompts in real-time based on model outputs or user inputs, enabling adaptive behavior without retraining.

“Dynamic prompting made the system detect when the model was uncertain and ask clarifying questions to improve accuracy.”

Origin: Emerged as an advanced technique by 2024.

Using multiple different prompts for the same task and combining their outputs to achieve more robust or accurate results.

“Prompt ensembling ran the question through three different prompts and took a majority vote, eliminating individual prompt weaknesses.”

Origin: Emerged from ensemble learning principles by 2023.

Training a smaller, faster model to mimic a larger model's outputs, enabling deployment where scale or latency is constrained.

“Knowledge distillation let us deploy a small model on mobile devices that performed almost as well as the large server-based model.”

Origin: Introduced by Hinton et al. in 2015; applied to language models by 2023.

Adjusting a general-purpose model to perform well in a specific domain through fine-tuning, prompting, or retrieval augmentation.

“Domain adaptation made the general model specialized for legal documents by fine-tuning on legal data and using legal-specific prompts.”

Origin: Standard technique in machine learning; applied to prompt engineering by 2023.

Explicitly instructing the model what NOT to do or generate, often improving output quality by avoiding common mistakes.

“Negation prompting said 'Don't make up sources' and 'Don't contradict the provided facts', reducing hallucinations.”

Origin: Emerged as a practical technique by 2023.

Asking the model to adopt different viewpoints or perspectives to generate more balanced, nuanced, or creative responses.

“Perspective prompting asked the model to respond as an optimist, pessimist, and realist, then synthesized the three viewpoints.”

Origin: Emerged as a creative technique by 2023.

Providing contrasting examples or scenarios to help the model understand nuances and make finer distinctions in responses.

“Contrastive prompting showed examples of good and bad customer service responses, helping the model learn the difference.”

Origin: Emerged as a refinement technique by 2023.

Using analogies or comparisons to help the model understand complex concepts or solve problems by drawing parallels.

“Analogical prompting explained machine learning by comparing it to how a child learns, helping the model generate better explanations.”

Origin: Emerged as a pedagogical technique by 2023.

Asking the model to reason about hypothetical or counterfactual scenarios to improve reasoning about causality and alternatives.

“Counterfactual prompting asked 'What if we had started with a different strategy?' to help the model consider alternative outcomes.”

Origin: Emerged from causal reasoning research by 2023.

Breaking a complex task into smaller, simpler subtasks that are easier for the model to solve individually.

“Decomposition broke the complex research task into finding sources, extracting key points, and synthesizing, each easier than the whole.”

Origin: Emerged as a problem-solving technique by 2023.

Organizing prompts in a hierarchy where high-level decisions guide lower-level details, enabling complex multi-level reasoning.

“Hierarchical prompting first decided the overall strategy, then planned specific steps, then executed each step with details.”

Origin: Emerged from hierarchical reasoning research by 2024.

Repeatedly generating, evaluating, and improving outputs through multiple passes to achieve higher quality results.

“Iterative refinement had the model draft an essay, critique it, revise it, critique again, and repeat until satisfied.”

Origin: Emerged as a best practice by 2023.

A dialogue where the user and model exchange multiple messages, maintaining context across turns to enable coherent extended interactions.

“In multi-turn conversation, the user asked a question, the model answered, the user asked a follow-up, and the model remembered the context.”

Origin: Standard feature in modern chatbots; formalized in API design by 2022.

Systematically examining model failures and mistakes to identify patterns and improve prompts or training.

“Error analysis revealed that the model consistently failed on questions with negations, so I added negation examples to the prompt.”

Origin: Standard software engineering practice; applied to prompt engineering by 2023.

Testing the impact of individual prompt components by removing them one at a time to identify which elements drive performance.

“Ablation study showed that removing the role prompt decreased accuracy by 5%, confirming it was essential.”

Origin: Standard research methodology; applied to prompt engineering by 2023.

A standardized test or dataset used to evaluate and compare model performance across different prompts, models, or approaches.

“We tested the prompt on the MMLU benchmark and achieved 85% accuracy, confirming it was working well.”

Origin: Standard evaluation term; applied to prompt engineering by 2022.

Using prompts in multiple languages or encouraging the model to reason in different languages to improve multilingual performance.

“Cross-lingual prompting provided examples in English and Spanish, improving the model's Spanish output quality.”

Origin: Emerged as models became more multilingual by 2023.

Incorporating structured knowledge from knowledge graphs into prompts to ground the model in verified facts and relationships.

“Knowledge graph prompting provided structured facts about a company's hierarchy and products, grounding the model's responses.”

Origin: Emerged as knowledge graphs became more integrated by 2024.

Prompts that encourage the model to apply commonsense reasoning to problems that require understanding of typical real-world situations.

“Commonsense prompting made the model reason that 'wet floors are slippery' without explicit instruction.”

Origin: Emerged from commonsense reasoning research by 2023.

Incorporating images, diagrams, or visual elements into prompts for multimodal models to improve understanding and reasoning.

“Visual prompting showed the model a diagram of the system architecture, helping it generate better documentation.”

Origin: Emerged as multimodal models became prevalent by 2023.

Connecting a language model to external APIs or services to access real-time data, tools, or functionality.

“API integration let the model call weather, stock, and news APIs, providing current information in responses.”

Origin: Standard software engineering practice; formalized in LLM applications by 2023.

Specifying how the model should structure its responses, such as bullet points, JSON, or markdown, for easier parsing and use.

“Output formatting told the model to respond with a JSON object containing fields like summary, key_points, and sentiment.”

Origin: Emerged as a practical necessity in production systems by 2022.

Specifying hard constraints or requirements that the model's output must satisfy, ensuring compliance with business or safety rules.

“Constraint satisfaction required all customer replies to include a satisfaction survey link and never exceed 200 words.”

Origin: Emerged as a reliability requirement by 2023.

Ensuring that model outputs are logically coherent and don't contradict facts or earlier statements in the same response.

“Semantic consistency checking found that the model claimed it was morning and evening in the same response, and corrected the inconsistency.”

Origin: Emerged as a quality concern by 2023.

Verifying that model outputs contain accurate information by cross-referencing against trusted sources or databases.

“Fact checking automatically verified that the model's claim about the capital of France was correct before returning the response.”

Origin: Emerged as a critical safety measure by 2023.

Adjusting the model's communication style or register to match a desired tone such as formal, casual, technical, or empathetic.

“Tone adaptation made the customer service model respond in a friendly, empathetic tone while keeping technical explanations precise.”

Origin: Emerged as a personalization technique by 2023.

Identifying what the user is trying to accomplish from their input, enabling more appropriate and targeted responses.

“User intent detection recognized that the user's question about returns was actually a frustration about a late delivery.”

Origin: Emerged from conversational AI research by 2023.

Checking that model outputs meet quality criteria such as factuality, safety, and format compliance before returning them to users.

“Response validation rejected outputs that violated safety policies or contained unsourced claims before they reached users.”

Origin: Emerged as a production necessity by 2023.

Summarizing or condensing earlier parts of a conversation when context windows fill up, maintaining key information while freeing space.

“Context collapsing summarized the first 50 messages into a brief recap when the context window was nearly full.”

Origin: Emerged as long conversations became common by 2024.

Training models to learn human preferences through pairwise comparisons or rankings of outputs, enabling alignment without explicit rules.

“Preference learning showed the model pairs of responses and which was better, teaching it to prefer helpful, harmless outputs.”

Origin: Emerged in RLHF research; formalized by 2022.

The degree to which a model generates varied, non-repetitive outputs across multiple requests or sampling attempts.

“Output diversity was improved by raising temperature and using diverse prompts, preventing the model from giving the same answer every time.”

Origin: Emerged as a quality concern by 2023.

Obscuring or hiding system prompts and sensitive information in deployed systems to prevent prompt injection or leaking attacks.

“Prompt obfuscation hid the system prompt from users to prevent them from extracting and misusing our proprietary instructions.”

Origin: Emerged as a security concern by 2023.

Reducing response time by optimizing prompts, caching results, or using faster models while maintaining output quality.

“Latency optimization reduced response time from 5 seconds to 1 second by shortening the prompt and using caching.”

Origin: Emerged as a production concern by 2023.

Optimizing prompts and model selection to minimize API costs while maintaining acceptable output quality.

“Cost efficiency was achieved by using a smaller, cheaper model with a well-tuned prompt instead of the most expensive option.”

Origin: Emerged as a business concern by 2023.