A model does not know events after its training ended. What is this limitation?
Why A is the answer
The knowledge cutoff
A model knows nothing after its knowledge cutoff, unless it is given newer data.
In detail
- The knowledge cutoff is where training data ended.
- Grounding addresses this.
Use case
An illustrative scenario, not a real incident.
A model knows nothing of last month's law change, which falls after its cutoff.
See it in different ways
flowchart TD A["Knowledge cutoff or hallucination"] --> B["Grounding and RAG"] C["Vague output"] --> D["Prompt engineering"] E["Specialized style"] --> F["Fine-tuning"] G["High-stakes decisions"] --> H["Human in the loop"]
stateDiagram-v2 [*] --> SourceOfFacts SourceOfFacts --> GoogleSearch: current public facts SourceOfFacts --> AgentSearch: company documents GoogleSearch --> GroundedAnswer AgentSearch --> GroundedAnswer GroundedAnswer --> HumanReview: high impact GroundedAnswer --> [*]
stateDiagram-v2 state "Training ends" as s0 state "Knowledge cutoff" as s1 state "New events unknown" as s2 state "Grounded with fresh data" as s3 [*] --> s0 s0 --> s1 s1 --> s2 s2 --> s3: grounding s3 --> [*]
Key point: Models know nothing past their cutoff.
Why the other options are wrong
- B. Fine-tuning
- Fine-tuning adapts a model.
- C. Grounding
- Grounding is the remedy.
- D. Temperature
- Temperature controls variety.