OCI 1Z0-1127-25 glossary of embeddings, vector search, prompt flows, and governance terms.
On this page
Use this glossary to clean up high-confusion OCI generative-AI terms before you go back into mixed sets. On this exam, terminology mistakes usually hide a systems-thinking mistake.
High-value terms
Term
What it means here
Why it matters on the exam
Embedding
a numeric representation that captures semantic similarity
weak embedding strategy often causes weak retrieval
Evaluation
the process of measuring usefulness, correctness, safety, or task fit
this exam expects evaluation by layer, not by vibe
Fine-tuning
additional training that changes model behavior beyond prompt-time controls
candidates often overuse it when grounding is enough
Grounding
supplying relevant external context so output is anchored to source material
grounding is a core tie-break against hallucination
Hallucination
unsupported, incorrect, or fabricated output that may still look fluent
fluency is not evidence
Inference
using a trained model to produce output from new input
many questions hinge on what happens at inference time
Prompt injection
hostile or manipulative instructions that try to override system behavior
retrieved documents can carry untrusted instructions
RAG-style flow
retrieval before generation so output is supported by source material
this is a frequent exam decision lane
Safety control
a rule, filter, permission, or process that reduces harmful output or leakage
safety is not just one blocking keyword list
Service wrapper
the product surface around a model capability
wrapper and underlying model behavior are not the same thing
Common confusion pairs
Pair
Clean separation
Grounding vs fine-tuning
grounding supplies context at inference time, fine-tuning changes model behavior through training
Inference vs training
inference produces output, training changes or builds the model
Prompt improvement vs model improvement
prompt improvement changes the request, model improvement changes the underlying system
Embedding vs generated answer
an embedding is a semantic representation, a generated answer is output text or content
Model capability vs service wrapper
the wrapper is the product surface, the capability is what the model can actually do
Safety control vs quality control
safety blocks or constrains harm, quality control measures whether the answer is good
Retrieval error vs generation error
retrieval error brings in bad context, generation error mishandles the available context
Fast recall anchors
If you see…
Think…
wrong documents
retrieval quality
fluent but unsupported answer
grounding and evaluation
bad output from risky source content
prompt injection or safety boundary
expensive or slow answer
context size, model fit, and delivery path
If three terms blur together
Terms
Short reset
grounding, retrieval, embedding
retrieval finds candidates, embeddings help similarity, grounding anchors generation with the chosen context
prompt engineering, fine-tuning, model choice
prompting changes the request, fine-tuning changes behavior, model choice changes base capability
safety, governance, evaluation
safety reduces harmful behavior, governance constrains who can do what, evaluation checks how well the system performs
inference, deployment, monitoring
inference is the model call, deployment is how the system is served, monitoring is how you watch it in operation