Response Conciseness

from uptrain import EvalLLM, Evals

OPENAI_API_KEY = "sk-********************"  # Insert your OpenAI key here

data = [{
    "question": "What are the primary components of a cell?",
    "response": "The primary components of a cell are crucial for its function. Speaking of components, the integration of software components in modern applications is a key challenge for developers. It requires careful consideration of architectural patterns and design principles."
}]

eval_llm = EvalLLM(openai_api_key=OPENAI_API_KEY)

res = eval_llm.evaluate(
    data = data,
    checks = [Evals.RESPONSE_CONCISENESS]
)

[
   {
      "score_response_conciseness": 0.0,
      "explanation_response_conciseness": "The LLM response contains a lot of additional irrelevant information because it completely deviates from the user query about the primary components of a cell. Instead of providing relevant information about cell components, the response talks about software integration, architectural patterns, and design principles, which are not related to the user query at all. This additional information is not needed to answer the user's question and only serves to confuse and distract from the main topic."
   }
]