Zero-Shot Learning
Zero-shot learning lets an AI model perform a new task or recognize an unseen category without task-specific labeled examples.
Explore AIstify's latest reporting, research, and expert analysis tagged with "prompt engineering", collected in one continuously updated archive.
Zero-shot learning lets an AI model perform a new task or recognize an unseen category without task-specific labeled examples.
OpenAI’s new GPT-5.6 prompting guide urges developers to write shorter, outcome-first prompts, saying leaner instructions raised scores 10-15% while cutting tokens and cost sharply.
Temperature is a generative AI setting that adjusts how strongly a model favors likely tokens, influencing output consistency and variation.
Prompt engineering is the practice of designing instructions, context, examples, and output rules that help generative AI perform tasks reliably.
Prompt injection is an AI security attack that uses malicious instructions to override a model’s rules, expose data, or trigger unauthorized tool actions.
Few-shot learning allows an AI model to recognize a task or pattern from only a small number of examples.
Negative prompting tells a generative AI model what to avoid in an image or other output, helping steer style, content, and unwanted artifacts.
In-context learning lets a language model infer a task from instructions or examples inside the prompt without changing the model’s trained parameters.
A context window is the amount of text and other tokenized information an AI model can consider during a single interaction.
OpenAI acknowledges prompt injection attacks cannot be fully eliminated and is using automated AI attackers to harden its Atlas agentic browser against threats.
Adobe’s Firefly app adds a new video editor supporting precise prompt-based edits, along with integration of third-party models including Black Forest Labs’ FLUX.2 and Topaz Astra for enhanced video and image generation.
An instruction or input given to an AI model that guides its response. Well-crafted prompts lead to more accurate, creative, and context-aware results in generative AI systems.