Purpose
This glossary defines AI engineering terminology used throughout the
Engineering Knowledge Framework.
Glossary
Context Engineering
| Property | Value |
|---|
| Definition | The practice of providing AI systems with structured, |
| relevant information to improve the quality and accuracy |
| of their output. |
| Context | Context engineering includes defining scope, |
| constraints, standards, examples, references and quality |
| criteria. It is the primary lever for improving AI |
| output quality. |
| Related | Prompt Engineering |
| References | AI Engineering Handbook |
Hallucination
| Property | Value |
|---|
| Definition | The tendency of AI systems to generate plausible but |
| incorrect information with high confidence. |
| Context | Hallucinations are a known limitation of large language |
| models. Mitigation requires verifying AI output against |
| authoritative sources and applying human review. |
| Related | Verification |
| References | AI Engineering Handbook |
Prompt Engineering
| Property | Value |
|---|
| Definition | The practice of designing input prompts to elicit |
| desired responses from AI systems. |
| Context | Prompt engineering is a subset of context engineering. |
| Effective prompts are specific, structured and include |
| relevant context. |
| Related | Context Engineering |
| References | AI Engineering Handbook |
Verification
| Property | Value |
|---|
| Definition | The process of evaluating AI-generated output for |
| correctness, consistency and safety before acceptance. |
| Context | Every AI output must be verified. Verification includes |
| testing code, checking facts, reviewing security |
| implications and evaluating against requirements. |
| Related | Hallucination |
| References | AI Engineering Handbook |