Google Cloud Generative AI Leader: key terms

Flashcards for this exam Google Cloud Generative AI Leader guide Class dates

• Agent Development Kit (ADK)Google's open-source framework for building, evaluating and deploying multi-agent systems.Advanced
• Agent2Agent Protocol (A2A)An open protocol, introduced by Google and now under the Linux Foundation, for communication and task delegation between AI agents.Advanced
• Agentic AIAI systems that act with autonomy to plan and execute multi-step tasks toward goals, rather than only responding to single prompts.Foundational
• AI AgentAn AI system that uses a model to pursue a goal autonomously by planning, calling tools and acting on results in a loop.Foundational
• Algorithmic BiasSystematic, unfair errors in AI outputs that disadvantage certain groups, often inherited from data.Foundational
• Artificial Intelligence (AI)The field of building computer systems that perform tasks normally requiring human intelligence, such as reasoning, perception and language.Foundational
• BenchmarkA standardized dataset and metric used to compare model capabilities.Intermediate
• BigQuery ML (BQML)A Google Cloud feature that lets users create and run ML models in BigQuery using SQL.Intermediate
• Chain-of-Thought Prompting (CoT)Prompting a model to reason step by step before giving a final answer.Intermediate
• Context WindowThe maximum number of tokens (input plus output) a model can consider in a single request.Foundational
• Diffusion ModelA generative model that learns to create data by reversing a gradual noising process.Intermediate
• Discriminative ModelA model that learns the boundary between classes, estimating P(label | input).Intermediate
• EmbeddingA dense numeric vector representing the meaning of text, images or other data so that similar items are close together.Foundational
• FairnessThe principle that AI systems should not produce unjustly different outcomes for individuals or groups.Foundational
• Few-Shot PromptingIncluding several examples of inputs and desired outputs in the prompt to guide the model.Foundational
• Fine-TuningFurther training a pretrained model on a smaller task- or domain-specific dataset to adapt its behavior.Intermediate
• Foundation Model (FM)A large model pretrained on broad data that can be adapted to many downstream tasks.Foundational
• Function CallingAn API feature where a model outputs a structured request to call a developer-defined function with arguments.Foundational
• GeminiGoogle's family of natively multimodal foundation models, available in the Gemini app, Gemini API and Vertex AI.Foundational
• Generative Adversarial Network (GAN)A generative model in which a generator and a discriminator are trained against each other.Intermediate
• Generative AI (GenAI)AI that creates new content such as text, images, code, audio or video based on patterns learned from training data.Foundational
• Generative Pre-trained Transformer (GPT)OpenAI's family of decoder-only transformer models pretrained on large text corpora for generation.Foundational
• Grounding (Generative AI)Anchoring a model's responses to verifiable source data supplied at runtime.Foundational
• HallucinationWhen a generative model produces fluent output that is false, fabricated or unsupported by its sources.Foundational
• In-Context Learning (ICL)A model's ability to learn a task from instructions and examples in the prompt without weight updates.Intermediate
• Inference ParametersSettings supplied at request time that shape model output, such as temperature, top-p, max tokens and stop sequences.Foundational
• Knowledge CutoffThe date after which a model has no information from its training data.Foundational
• Large Language Model (LLM)A very large transformer model trained on massive text data to understand and generate natural language and code.Foundational
• Multimodal ModelA model that can process or generate more than one data type, such as text, images, audio and video.Foundational
• Open-Weight ModelA model whose trained weights are publicly downloadable for self-hosting and customization.Intermediate
• PretrainingThe initial, compute-intensive training of a model on vast unlabeled data to learn general representations.Intermediate
• PromptThe input text (and optionally images or files) given to a generative model to direct its output.Foundational
• Prompt EngineeringDesigning and refining model inputs to reliably elicit accurate, useful and safe outputs.Foundational
• Provisioned ThroughputReserved model inference capacity purchased for consistent performance, rather than paying per token on demand.Intermediate
• Reasoning ModelAn LLM trained to generate intermediate reasoning before answering, improving performance on complex multi-step tasks.Intermediate
• Reinforcement Learning from Human Feedback (RLHF)Aligning a model by training a reward model on human preference rankings and optimizing the model against it.Intermediate
• Responsible AIDeveloping and using AI in ways that are fair, reliable, safe, private, secure, transparent, accountable and inclusive.Foundational
• Retrieval-Augmented Generation (RAG)Retrieving relevant information from external sources at query time and adding it to the prompt so the model answers from it.Foundational
• Semantic SearchSearch that retrieves results by meaning, using embeddings, rather than by exact keyword match.Foundational
• Small Language Model (SLM)A language model with relatively few parameters (typically under ~15B) designed for efficiency, edge or cost-sensitive use.Intermediate
• SummarizationCondensing a document into a shorter version that preserves key information.Foundational
• TemperatureAn inference parameter that scales randomness in token selection; low values are more deterministic, high values more creative.Foundational
• Tensor Processing Unit (TPU)Google's custom ASIC for accelerating neural network training and inference, available on Google Cloud.Intermediate
• Test-Time ComputeAdditional computation spent during inference, such as longer reasoning or multiple samples, to improve answer quality.Advanced
• Text-to-ImageGenerating images from natural-language descriptions.Foundational
• TokenThe basic unit of text an LLM processes, typically a word, subword, character or punctuation mark.Foundational
• Token PricingCharging for LLM usage per input and output token, often with separate rates for cached and batched tokens.Foundational
• Tokenization (NLP)Splitting text into tokens that a model can convert to numeric IDs.Foundational
• Top-k SamplingSampling the next token only from the k most probable candidates.Intermediate
• Top-p SamplingNucleus sampling: choose the next token from the smallest set whose cumulative probability exceeds p.Intermediate
• TransformerA neural network architecture based on self-attention that processes all tokens in a sequence in parallel; the basis of modern LLMs.Foundational
• TransparencyOpenness about how an AI system works, its data, limitations and when people are interacting with AI.Foundational
• Vector DatabaseA database optimized for storing embeddings and performing fast similarity search over them.Foundational
• Vertex AIGoogle Cloud's unified AI platform for building, deploying and scaling ML models and generative AI applications and agents.Foundational
• Vision-Language Model (VLM)A model that jointly understands images and text, for tasks such as visual question answering and captioning.Intermediate
• WatermarkingEmbedding detectable signals in AI-generated content to identify it as machine-made.Intermediate
• Zero-Shot PromptingAsking a model to perform a task with instructions only and no examples.Foundational