evaluation-methodology

name	evaluation-methodology
description	Methods for evaluating AI model outputs - exact match, semantic similarity, LLM-as-judge, comparative evaluation, ELO ranking. Use when measuring AI quality, building eval pipelines, or comparing models. Use when this capability is needed.
metadata	{"author":"doanchienthangdev"}

Evaluation Methodology

Methods for evaluating Foundation Model outputs.

Evaluation Approaches

1. Exact Evaluation

Method	Use Case	Example
Exact Match	QA, Math	"5" == "5"
Functional Correctness	Code	Pass test cases
BLEU/ROUGE	Translation	N-gram overlap
Semantic Similarity	Open-ended	Embedding cosine

# Semantic Similarity
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity

model = SentenceTransformer('all-MiniLM-L6-v2')
emb1 = model.encode([generated])
emb2 = model.encode([reference])
similarity = cosine_similarity(emb1, emb2)[0][0]

2. AI as Judge

JUDGE_PROMPT = """Rate the response on a scale of 1-5.

Criteria:
- Accuracy: Is information correct?
- Helpfulness: Does it address the need?
- Clarity: Is it easy to understand?

Query: {query}
Response: {response}

Return JSON: {"score": N, "reasoning": "..."}"""

# Multi-judge for reliability
judges = ["gpt-4", "claude-3"]
scores = [get_score(judge, response) for judge in judges]
final_score = sum(scores) / len(scores)

3. Comparative Evaluation (ELO)

COMPARE_PROMPT = """Compare these responses.

Query: {query}
A: {response_a}
B: {response_b}

Which is better? Return: A, B, or tie"""

def update_elo(rating_a, rating_b, winner, k=32):
    expected_a = 1 / (1 + 10**((rating_b - rating_a) / 400))
    score_a = 1 if winner == "A" else 0 if winner == "B" else 0.5
    return rating_a + k * (score_a - expected_a)

Evaluation Pipeline

1. Define Criteria (accuracy, helpfulness, safety)
   ↓
2. Create Scoring Rubric with Examples
   ↓
3. Select Methods (exact + AI judge + human)
   ↓
4. Create Evaluation Dataset
   ↓
5. Run Evaluation
   ↓
6. Analyze & Iterate

Best Practices

1. Use multiple evaluation methods
2. Calibrate AI judges with human data
3. Include both automatic and human evaluation
4. Version your evaluation datasets
5. Track metrics over time
6. Test for position bias in comparisons

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