Retrieval Augmented Generation (RAG) is a technique that supplies a foundation model with relevant retrieved documents at inference time so its answers are grounded in your data. AIF-C01 contrasts RAG with fine-tuning as ways to customize model behavior.
A foundation model is a large machine-learning model pre-trained on broad data that can be adapted to many tasks such as text generation, summarization, and Q&A.
Amazon Bedrock is a fully managed service that provides access to foundation models from multiple providers through a single API, with features for RAG, agents, and guardrails.
Prompt engineering is the practice of designing inputs to guide a foundation model toward the desired output using instructions, examples, and context.
Retrieval Augmented Generation (RAG) is a technique that supplies a foundation model with relevant retrieved documents at inference time so its answers are grounded in your data.
Fine-tuning is the process of further training a foundation model on labeled task-specific data to specialize it.
Amazon SageMaker is AWS's managed platform for building, training, and deploying machine-learning models.
Machine learning is a branch of AI in which systems learn patterns from data rather than being explicitly programmed.
Inference is the process of using a trained model to make predictions or generate output on new input data.
A hallucination is a confident but incorrect or fabricated output from a generative model.
Responsible AI is the practice of building AI systems that are fair, transparent, robust, and safe.
Amazon SageMaker Clarify is a tool that helps detect bias in data and models and explains model predictions.
An embedding is a numerical vector representation of text, images, or other data that captures semantic meaning for similarity search and retrieval.
A token is the basic unit of text a foundation model processes, roughly a word fragment.