Evaluation | HyperKit.ai

Definition

Evaluation is the process of measuring how well an LLM performs on specific tasks or criteria. This includes automated metrics, benchmark tests, and human assessments to understand model capabilities, limitations, and fitness for particular use cases.

Effective evaluation is crucial for model selection, development decisions, and ensuring quality in production.

Key Concepts

Automated metrics: Programmatic measures (BLEU, ROUGE, accuracy)
Human evaluation: Expert or crowdsourced quality ratings
LLM-as-judge: Using another LLM to evaluate outputs
Evals framework: Systematic evaluation pipelines

Examples

Types

Evaluation Approaches

EVALUATION TAXONOMY:

1. INTRINSIC EVALUATION:
   Measures model properties directly
   - Perplexity (language modeling)
   - Loss on validation set
   - Calibration (confidence = accuracy)

2. EXTRINSIC EVALUATION:
   Measures performance on downstream tasks
   - QA accuracy
   - Summarization quality
   - Code correctness (pass@k)

3. AUTOMATED METRICS:
   ┌─────────────┬─────────────────────────┐
   │ Metric      │ What it measures        │
   ├─────────────┼─────────────────────────┤
   │ Accuracy    │ Exact match rate        │
   │ F1 Score    │ Precision/recall balance│
   │ BLEU        │ N-gram overlap (trans.) │
   │ ROUGE       │ Overlap (summarization) │
   │ Pass@k      │ Code correctness        │
   │ Perplexity  │ Language model quality  │
   └─────────────┴─────────────────────────┘

4. HUMAN EVALUATION:
   - Pairwise comparison (A vs B)
   - Likert scale ratings (1-5)
   - Task completion rate
   - Annotation of errors

5. LLM-AS-JUDGE:
   Use GPT-4/Claude to rate outputs
   "Rate this response 1-10 for helpfulness"
   Cheaper than humans, correlates well
   But: potential biases (length, style)

Framework

Building an Eval Pipeline

EVALUATION FRAMEWORK STRUCTURE:

# Example using OpenAI Evals pattern
evals/
├── my_eval.yaml          # Eval definition
├── data/
│   └── test_cases.jsonl  # Test inputs/outputs
└── criteria/
    └── grading.py        # Scoring logic

EVAL DEFINITION (YAML):
name: customer_support_quality
description: Evaluate customer support responses
dataset: ./data/support_queries.jsonl
metrics:
  - accuracy      # factual correctness
  - helpfulness   # rated 1-5
  - tone          # appropriate/not

TEST CASE FORMAT (JSONL):
{"input": "How do I reset my password?",
 "expected_topics": ["password", "reset", "email"],
 "expected_action": "provide_instructions"}
{"input": "I'm very frustrated with your service!",
 "expected_topics": ["empathy", "resolution"],
 "expected_tone": "apologetic, helpful"}

SCORING IMPLEMENTATION:

def evaluate_response(input, output, expected):
    scores = {}

    # Automated checks
    scores['contains_keywords'] = check_topics(
        output, expected['expected_topics']
    )

    # LLM-as-judge
    scores['helpfulness'] = llm_judge(
        prompt=f"Rate helpfulness 1-5:\n{output}",
        rubric="5=fully resolves, 1=unhelpful"
    )

    # Aggregate
    scores['overall'] = weighted_average(scores)
    return scores

RUNNING EVALS:
results = eval_runner.run(
    model="gpt-4",
    eval_name="customer_support_quality",
    sample_size=500
)
print(f"Accuracy: {results.accuracy:.2%}")
print(f"Helpfulness: {results.helpfulness:.1f}/5")

Interactive Exercise

✎

Choose Evaluation Methods

For each use case, what evaluation method would be most appropriate and why?

1. Translation quality

2. Creative writing

3. Code generation

Pro Tips

Automated metrics are fast but may not capture what humans care about
LLM-as-judge is cost-effective but can have systematic biases
Always validate automated evals against human judgment
Track evaluation metrics over time, not just at launch

Definition

Key Concepts

Examples

Interactive Exercise

Related Terms