A–Z index

Na how you coordinate agents, tools, and work steps so dem fit work together in the right order.

Na coding work style wey AI agent dey plan and carry out plenty development steps, while person dey supervise as the work need.

Na AI system wey fit work toward one goal by choosing and doing steps. Sometimes e dey use tools or services, with person no need to direct am every second.

Na when you use AI tool to check code for possible mistake, security wahala, readability problem, or improvement.

Na tool wey use AI to help developer write, understand, or change code.

Na test wey you fit repeat to measure how well AI model or app dey perform for selected task or criteria.

Na system wey dem don train with data; e use wetin e learn to make prediction, choose answer, or create content.

Na spoken audio wey AI create or adjust for narration or other voice content.

Na set of connected steps wey AI model, tools, and people dey use together to finish work.

Na when person use AI tools to help write, explain, test, find bug, or improve code, while person still dey guide and review the work.

Na test cases or test code wey AI create to check whether software dey behave as expected.

Na clear steps or rules wey computer dey follow to solve problem or finish one work.

Na clear way wey software systems dey use to ask each other for data or action.

Na computer system wey dem design to do some work wey normally need human sense, like to notice patterns, understand language, or suggest wetin person fit do.

Na relationship between picture or video width and height, like 16:9 or 9:16.

Na set of data or tasks wey dem define to compare how different models or systems perform.

Na when data or AI dey lean one side in a regular way, wey fit make some people or groups get unfair or less correct result.

Na task wey AI dey put something inside one or more group wey dem don decide before, like mark email as spam or no be spam.

Na AI feature wey dey suggest wetin fit come next as person dey type code.

Na when AI produce source code from prompt, specification, or the code wey surround am.

Na AI system wey fit inspect codebase and do software work, sometimes by changing files or running tools.

Na area of AI wey dey help computer check and understand picture and video.

Na when you choose and arrange project information, instruction, and tools wey AI need to do work well.

Na total amount of text or other information wey AI model fit consider for one time, including the conversation wey matter.

Na method wey guide image generation with extra visual structure, like pose, outline, or depth.

Na when private or restricted information escape by mistake through AI system, training data, or answers.

Na collection of data wey dem arrange together to train, test, or check how AI dey perform.

Na when you find, understand, and fix mistake wey make software no dey work as e suppose.

Na when you divide difficult request into smaller tasks wey AI fit handle more easily.

Na machine learning wey use neural network wey get plenty layers to learn difficult patterns from plenty data.

Na fake or changed media wey convincingly show person dey talk or do something wey e no talk or do.

Na model wey dem train to create new data by learning how to reverse the process wey dey add noise little by little.

Na set of numbers wey represent meaning or features of text, picture, or other data, so computer fit compare dem with calculation.

Na when dem test AI with clear measures to see how correct, useful, safe, or dependable e be.

Na how well person fit understand why AI produce one particular answer or decision.

Na prompt wey get small number of examples to guide model make e do task or follow answer format.

Na when dem train model wey don already learn before with selected examples again, so e go fit one particular work or style better.

Na one still picture wey show one moment inside video.

Na when model create extra frames between existing ones to make movement smooth or increase video frame rate.

Na AI wey fit create new things like text, picture, sound, video, or code, based on pattern wey e don learn from data.

Na data wey get correct reference answers or labels wey dem use to check AI output.

Na when you give AI useful, trusted information make the answer connect to wetin you provide.

Na rules and technical checks wey dey control wetin AI fit do, to reduce answer or action wey no good or fit cause wahala.

Na code wey AI produce wey look correct, but e fit use function wey no dey, wrong API, or bad logic.

Na when AI talk something with confidence, but the information no true, no get evidence, or na im make am up.

Na when people check AI answers with clear criteria like correctness, clarity, or usefulness.

Na way of using AI where person still dey check, guide, or approve important part of the work or the result.

Na when model fill or change the part of picture wey you select, often based on prompt.

Na when you increase picture resolution; sometimes AI dey add or rebuild small details as e dey do am.

Na when you give model existing picture make e change am or create another picture wey connect to am.

Na when model use one still picture as starting point to create or animate video.

Na when you use AI model wey don train to produce answer, prediction, or put something inside one group.

Na selected frame wey show important pose, position, or moment wey guide animation between frames.

Na AI model wey dem train with plenty text so e fit understand and produce language. E dey use wetin e learn and the question wey you ask am to form likely answer.

Na compressed number-representation wey generative model fit use to work with important features of data.

Na when mouth movement for video dey match the words wey person talk for audio.

Na small set of model adjustments wey fit teach big model one subject, style, or task, without retraining the whole model.

Na one way to build AI wey dey learn pattern from examples or data, instead of following only rules wey person write by hand.

Na open protocol wey let AI applications connect to outside tools and data sources through one common interface.

Na system wey plenty AI agents dey coordinate or share work to reach one goal or related goals.

Na AI wey fit work with more than one kind information, like text, picture, sound, or video.

Na area of AI wey dey help computer work with human language, like to read, understand, or produce text and speech.

Na words or instruction wey tell image model wetin e no suppose put inside the picture.

Na computer method wey get connected units for different layers. E dey learn how things for data relate; the idea get small likeness to how brain dey work.

Na prompt wey get one example to show model the kind work or answer format wey you expect.

Na AI software or model parts wey dem release with materials and permission wey fit let people inspect, use, change, or share am. Wetin you fit do depend on the license.

Na when model extend picture pass the original edge and create new part wey match the scene.

Na guess about wetin fit happen or result wey person never know, based on pattern wey dey inside available data.

Na how dem dey protect and handle information about people well, including how dem collect, use, keep, and share am.

Na instruction, question, or information wey you give AI to guide the answer wey e go give you.

Na when you divide big work into prompts, then use answer from one step as input for the next step.

Na how person dey write and adjust instruction so AI go get better chance to give useful answer.

Na attempt to make AI ignore or change im instruction by putting conflicting command inside user input or information wey e retrieve.

Na prompt wey you fit reuse, with fixed instruction and blank spaces wey you go fill with different information.

Na system wey dey suggest things or action wey person fit like or find useful, based on things wey e choose before or wetin similar people like.

Na when people purposely test AI with difficult or attack-style input to find weakness and risk.

Na when you change how code arrange am to make e clearer or easier to maintain, without changing wetin e suppose do.

Na picture wey you give model to guide how the generated content go look, the subject, arrangement, or style.

Na information about project files, arrangement, coding style, and dependencies wey help AI produce code wey fit the project.

Na when dem design and use AI with care for safety, fairness, privacy, reliability, clear explanation, and how e go affect people.

Na method wey first find useful information from trusted source, then give the information to language model to help am answer.

Na when you give model role or point of view, like “act as patient teacher,” to shape the answer.

Na separate environment wey you fit run or test code inside, with limited access to other system or files.

Na number wey set the random starting point for generation. If you reuse am with the same settings, e fit help produce similar result.

Na software work style wey use clear written specification to guide planning, AI code, and checking.

Na AI answer wey follow one format wey dem set, like JSON, table, or list of named fields.

Na when you apply the look or style of one picture to another picture, while you keep some of the original content.

Na important instruction wey set how model go behave, the role wey e get, or the limits wey e must follow for conversation or app.

Na model setting wey fit affect how varied or predictable the generated text go be; how e work depend on the model.

Na how well generated video dey keep people, objects, light, and movement consistent from one frame go another.

Na when image model use the description wey you write to create picture.

Na when model use description wey you write to create video.

Na small piece of text wey language model dey process. E fit be full word, part of word, or punctuation sign; e no always be one complete word.

Na when model use outside function or service to do action or get information.

Na generation setting wey limit the possible next tokens to a group based on probability before model choose one.

Na examples and information wey dem give AI make e learn pattern or how to do one task.

Na database wey dem design to keep and search number-representation of data, usually to find things wey meaning dey similar.

Na style of building software wey you dey explain wetin you want with normal language, then direct AI coding tool step by step and check whether the result dey work.

Na when model change existing video into another version, often based on prompt or visual style.

Na visible or hidden mark wey dem add to content to show where e come from, who own am, or say AI generate am.

Na prompt wey ask model to do work without showing am examples of the kind answer wey you want.