Real Answer: No
Predicted Answer:  No, this sentence is not plausible. Joao Moutinho is a professional soccer player, not an American football player, so it is not likely that he would be catching a screen pass in the NFC championship.
Predicted Grade:  CORRECT
Customize Prompt#
You can also customize the prompt that is used. Here is an example prompting it using a score from 0 to 10.
The custom prompt requires 3 input variables: “query”, “answer” and “result”. Where “query” is the question, “answer” is the ground truth answer, and “result” is the predicted answer.
from langchain.prompts.prompt import PromptTemplate
_PROMPT_TEMPLATE = """You are an expert professor specialized in grading students' answers to questions.
You are grading the following question:
{query}
Here is the real answer:
{answer}
You are grading the following predicted answer:
{result}
What grade do you give from 0 to 10, where 0 is the lowest (very low similarity) and 10 is the highest (very high similarity)?
"""
PROMPT = PromptTemplate(input_variables=["query", "answer", "result"], template=_PROMPT_TEMPLATE)
evalchain = QAEvalChain.from_llm(llm=llm,prompt=PROMPT)
evalchain.evaluate(examples, predictions, question_key="question", answer_key="answer", prediction_key="text")
Evaluation without Ground Truth#
Its possible to evaluate question answering systems without ground truth. You would need a "context" input that reflects what the information the LLM uses to answer the question. This context can be obtained by any retreival system. Here’s an example of how it works:
context_examples = [
    {
        "question": "How old am I?",
rtdocs_stable/api.python.langchain.com/en/stable/use_cases/evaluation/question_answering.html