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Python / Python Modern Generative AI and Agents Interview Questions

How do you evaluate LLM outputs for quality, factual accuracy, and hallucination?

Traditional NLP metrics like BLEU and ROUGE measure surface-level token overlap but correlate poorly with human quality judgments for open-ended generation. Modern LLM evaluation uses a combination of reference-based metrics, LLM-as-judge evaluation, and task-specific benchmarks.

LLM Evaluation Methods
MethodWhat it measuresLimitation
BLEU / ROUGEN-gram overlap with reference textPoor correlation with quality for open-ended generation
BERTScoreSemantic similarity using BERT embeddingsMisses factual accuracy
LLM-as-judgeGPT-4 / Claude rates responses for quality, accuracy, relevanceBias toward verbose responses; expensive
Faithfulness (RAG)Is every claim in the answer supported by retrieved context?Requires context; slow to compute
Hallucination detectionNLI model checks if claim entails or contradicts sourceNLI models may themselves be wrong
Benchmark suitesMMLU, HumanEval, MT-Bench — standardised task batteriesMay not reflect domain-specific needs
# ── RAGAS: RAG evaluation framework
# pip install ragas
from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy, context_recall
from datasets import Dataset

# Prepare evaluation data
eval_data = {
    'question':  ['What is RAG?', 'Who created Python?'],
    'answer':    ['RAG is retrieval augmented generation.',
                  'Python was created by Guido van Rossum.'],
    'contexts':  [['RAG combines retrieval with generation...'],
                  ['Guido van Rossum created Python in 1991...']],
    'ground_truth': ['RAG stands for Retrieval Augmented Generation.',
                     'Guido van Rossum invented Python.'],
}
dataset = Dataset.from_dict(eval_data)
results = evaluate(dataset, metrics=[faithfulness, answer_relevancy])
print(results)  # {'faithfulness': 0.95, 'answer_relevancy': 0.91}

# ── LLM-as-judge (simple implementation)
from openai import OpenAI
client = OpenAI()

JUDGE_PROMPT = '''Rate the following answer for factual accuracy on a scale 1-5.
Question: {question}
Answer: {answer}

Return only a JSON: {{"score": <1-5>, "reason": "<brief reason>"}}'''

def llm_judge(question: str, answer: str) -> dict:
    import json
    resp = client.chat.completions.create(
        model='gpt-4o',
        messages=[{'role': 'user',
                   'content': JUDGE_PROMPT.format(question=question, answer=answer)}],
        temperature=0,
        response_format={'type': 'json_object'},
    )
    return json.loads(resp.choices[0].message.content)

Why is LLM-as-judge evaluation preferred over BLEU/ROUGE for modern LLM output assessment?
What does 'faithfulness' measure in RAG evaluation frameworks like RAGAS?

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