Is Concise

Evaluates whether the response is concise and to the point 

result = evaluator.evaluate(
    eval_templates="is_concise",
    inputs={
        "output": "Honey doesn't spoil because its low moisture and high acidity prevent the growth of bacteria and other microbes."
    },
    model_name="turing_flash"
)

print(result.eval_results[0].output)
print(result.eval_results[0].reason)
import { Evaluator, Templates } from "@future-agi/ai-evaluation";

const evaluator = new Evaluator();

const result = await evaluator.evaluate(
  "is_concise",
  {
    output: "Honey doesn't spoil because its low moisture and high acidity prevent the growth of bacteria and other microbes."
  },
  {
    modelName: "turing_flash",
  }
);

console.log(result);
Input
Required InputTypeDescription
outputstringGenerated content by the model to be evaluated for conciseness
Output
FieldDescription
ResultReturns Passed if the content is concise, or Failed if it’s not
ReasonProvides a detailed explanation of the evaluation

What to Do When Is Concise Score is Low

  • Remember that conciseness depends on context - what’s concise for a complex topic might still be relatively lengthy
  • This evaluation works best on complete responses rather than fragments
  • Very short responses may be marked as concise but might fail other evaluations like completeness
  • Consider the balance between conciseness and adequate information - extremely brief responses might miss important details

Comparing Is Concise with Similar Evals

  • Completeness: Is Concise evaluates brevity and avoidance of redundancy, while Completeness ensures the response addresses all aspects of the query.
  • Is Helpful: Is Concise focuses on avoiding unnecessary verbosity, while Is Helpful evaluates whether the response actually answers the user’s question effectively.
  • Instruction Adherence: Is Concise measures response length quality, while Instruction Adherence checks if the response follows specific instructions that might include length or detail requirements.
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