AWS AIF-C01 glossary of AI, ML, GenAI, foundation models, and governance terms.
Use this glossary when AI, ML, and generative AI terms start to blur together. Keep it beside the cheat sheet and resources instead of using it as a substitute for study.
| Term | Short meaning | Why it matters on AIF-C01 |
|---|---|---|
| Prompt | Input instruction given to a model | Core generative-AI control surface |
| Hallucination | Fluent but incorrect or unsupported model output | One of the most tested generative-AI risks |
| RAG | Retrieval-augmented generation, where a model uses retrieved external context | Common “improve grounding without retraining” answer |
| Fine-tuning | Additional training that adapts a model to a narrower task or style | Often contrasted with prompting and RAG |
| Embedding | Numeric vector representation of text or other content | Core to retrieval and semantic similarity |
| Vector store | System used to store and search embeddings | Retrieval layer for many RAG patterns |
| Guardrail | Safety or policy control for model inputs and outputs | Strong answer for policy and safety controls |
| Inference | Running a trained or hosted model to produce output | Distinct from training or adaptation |
| Supervised learning | Training from labeled examples | Foundational ML term the exam still expects |
| Foundation model | Broad pre-trained model adaptable to many tasks | Base concept for the Bedrock and GenAI sections |
| Bedrock | AWS managed service for foundation-model access and GenAI building blocks | Central AWS service for AIF-C01 service-fit questions |
| SageMaker AI | AWS managed platform for ML development and operations | Helps separate foundational ML from GenAI product fit |
| Token | Unit of text a model processes | Shows up in cost, context-window, and latency discussions |
| Context window | Amount of input/output context a model can consider in one interaction | Helps explain prompt and retrieval limits |
| Grounding | Anchoring model output to trusted source material | Strong answer when accuracy or factuality matters |
| Human-in-the-loop | Human review or approval before or after model output is used | Common control for high-risk outputs |
| Prompt injection | Malicious or adversarial instruction that tries to override system behavior | Important safety concept in GenAI systems |
| PII | Personally identifiable information | Core privacy and governance concept |
| Pair | Keep this distinction clear |
|---|---|
| prompting vs fine-tuning | change the instruction versus change the model behavior |
| RAG vs fine-tuning | retrieve external knowledge versus retrain or adapt the model |
| model inference vs model training | using the model versus creating or updating it |
| guardrail vs IAM policy | model-safety behavior control versus AWS access control |
| hallucination vs bias | unsupported output versus skewed or unfair output pattern |
| token vs embedding | unit of model text processing versus vector representation for similarity and retrieval |
| Bedrock vs SageMaker AI | managed FM access and GenAI building blocks versus broader ML development and operations platform |
| grounding vs fine-tuning | improve answers with trusted retrieved context versus alter model behavior through additional training |
| Cluster | Fast separation |
|---|---|
| prompt / RAG / fine-tuning | instruction change, retrieval-backed context, or model adaptation |
| hallucination / bias / privacy issue | wrong answer, unfair pattern, or exposed sensitive data |
| guardrail / IAM / encryption | behavior policy, access control, or data protection |
| token / embedding / vector store | text unit, numeric representation, or storage/search layer |
| Bedrock / SageMaker AI / prebuilt AI service | foundation-model platform, ML development platform, or task-specific managed service |
| Cluster | What it usually signals on the exam |
|---|---|
| prompting / context / grounding | answer quality and factuality controls |
| RAG / fine-tuning / customization | which adaptation strategy matches the requirement |
| guardrails / human review / monitoring | safety and responsible-use controls |
| IAM / KMS / audit trail | access, protection, and governance controls |
| Bedrock / SageMaker AI / prebuilt AI services | service-fit and implementation-scope judgment |
| Topic family | Best page to revisit |
|---|---|
| service fit and high-confusion pairs | Cheat Sheet |
| current AWS facts and official prep links | Resources |
| pacing and review order | Study Plan |
| overall exam framing | Guide root |
| RAG, prompting, and FM application choices | 3. Applications of Foundation Models |
| responsible AI and governance controls | 4. Guidelines for Responsible AI and 5. Security, Compliance, and Governance for AI Solutions |