AWS Certified AI Practitioner (AIF-C01) glossary
Terms selected for AWS Certified AI Practitioner (AIF-C01) based on common objective language and practice focus.
Artificial Intelligence (AI)
Broad field of building systems that perform tasks requiring human-like intelligence, such as reasoning, language understanding, and decision support.
Read full term ->Machine Learning (ML)
Subset of AI where systems learn patterns from data to make predictions or decisions without explicit rule coding for every case.
Read full term ->Generative AI
AI capability that creates new content such as text, images, code, or audio based on prompts and learned patterns.
Read full term ->Foundation Model (FM)
Large pre-trained model that can be adapted or prompted for many downstream tasks.
Read full term ->Prompt Engineering
Process of designing clear, structured prompts and constraints to improve model output quality.
Read full term ->Hallucination
When a model generates fluent but incorrect or unsupported content.
Read full term ->Retrieval-Augmented Generation (RAG)
Pattern that retrieves trusted context at query time and injects it into prompts to ground model responses.
Read full term ->Embedding
Numerical representation of text or other data that captures semantic meaning for search and similarity tasks.
Read full term ->Vector Database
Storage optimized for high-dimensional vectors and similarity search.
Read full term ->Fine-Tuning
Additional model training on task-specific data to improve behavior for a target domain.
Read full term ->Inference
Process of running a trained model to generate predictions or outputs for new inputs.
Read full term ->Model Evaluation
Assessing model quality using metrics, test sets, and qualitative review for task fitness.
Read full term ->Overfitting
When a model learns training data too closely and performs poorly on new data.
Read full term ->Bias and Fairness
Concern that models may produce systematically skewed outcomes across groups due to data or design issues.
Read full term ->Responsible AI
Practices that promote safety, transparency, accountability, privacy, and fairness throughout the AI lifecycle.
Read full term ->Amazon Bedrock
Managed AWS service for building generative AI applications with foundation models through APIs.
Read full term ->Amazon SageMaker
AWS platform for building, training, tuning, and deploying machine learning models.
Read full term ->Guardrails
Controls that constrain model behavior, such as topic restrictions, harmful-content filters, and output policies.
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