| name | model-evaluation-specialist |
| description | Advanced model evaluation covering LLM benchmarks, evaluation frameworks (lm-evaluate-harness, HELM, RAGAS), leaderboard interpretation, custom metrics design, human evaluation protocols, automated LLM-as-judge patterns, and evaluation pipeline architecture for both traditional ML and generative AI systems.
Use when the user asks about model evaluation specialist, model evaluation specialist best practices, or needs guidance on model evaluation specialist implementation.
Do NOT use when the user needs a different specialized skill or is asking about an unrelated technology domain.
|
| license | Apache-2.0 |
| metadata | {"author":"foundry-skills","version":"1.0.0","tags":"ai-ml testing guide","category":"ai-machine-learning","subcategory":"ml-fundamentals","depends":"","disclaimer":"none","difficulty":"intermediate"} |
Model Evaluation Specialist
Overview
Model evaluation for modern AI extends beyond accuracy metrics. LLMs and generative models require multi-dimensional assessment: factuality, reasoning, instruction following, safety, and human preference alignment. This skill covers evaluation frameworks, benchmark suites, custom metrics, LLM-as-judge patterns, and reproducible evaluation pipelines.
LLM Benchmark Landscape
| Benchmark | Measures | Tasks | Scoring |
|---|
| MMLU | Broad knowledge | 57 subjects, multiple choice | Accuracy |
| HumanEval / MBPP | Code generation | Python problems | pass@k |
| GSM8K | Math reasoning | Grade school word problems | Exact match |
| MT-Bench | Instruction following | 80 multi-turn questions | LLM judge 1-10 |
| AlpacaEval | Instruction following | 805 instructions | Win rate |
| TruthfulQA | Factuality | 817 misconception questions | Accuracy |
| HELM | Holistic evaluation | Multiple scenarios | Multi-metric |
| IFEval | Instruction compliance | Verifiable constraints | Compliance rate |
| WildBench | Real-world tasks | User-submitted prompts | LLM pairwise |
Benchmark Selection Guide
General-purpose LLM:
Quick ranking -> AlpacaEval or MT-Bench
Comprehensive -> HELM or custom suite
Knowledge -> MMLU + TruthfulQA
Code generation:
Function completion -> HumanEval (pass@1, pass@10)
Multi-file -> SWE-bench
Reasoning:
Math -> GSM8K + MATH
Logic -> BigBench-Hard + ARC-Challenge
RAG system:
Retrieval quality -> BEIR
End-to-end -> RAGAS framework
Chat / assistant:
Single-turn -> AlpacaEval
Multi-turn -> MT-Bench
Real-world -> WildBench + Arena Elo
Evaluation Frameworks
lm-evaluate-harness
import lm_eval
results = lm_eval.simple_evaluate(
model="hf",
model_args="pretrained=meta-llama/Llama-3-8B",
tasks=["mmlu", "hellaswag", "arc_challenge", "truthfulqa_mc2"],
batch_size=8,
num_fewshot=5,
)
for task, metrics in results["results"].items():
print(f"{task}: {metrics}")
RAGAS (RAG Evaluation)
from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy, context_precision, context_recall
from datasets import Dataset
def evaluate_rag(questions, answers, contexts, ground_truths) -> dict:
dataset = Dataset.from_dict({
"question": questions, "answer": answers,
"contexts": contexts, "ground_truth": ground_truths,
})
result = evaluate(dataset, metrics=[
faithfulness, answer_relevancy, context_precision, context_recall,
])
return dict(result)
| RAGAS Metric | Measures | Good Score |
|---|
| Faithfulness | Answer grounded in context? | >0.85 |
| Answer Relevancy | Addresses the question? | >0.80 |
| Context Precision | Retrieved docs relevant? | >0.75 |
| Context Recall | All needed docs retrieved? | >0.80 |
LLM-as-Judge Patterns
Pairwise Comparison
from openai import OpenAI
import json
class LLMJudge:
def __init__(self, judge_model: str = "gpt-4o"):
self.client = OpenAI()
self.model = judge_model
def pairwise_compare(self, prompt: str, response_a: str, response_b: str, criteria: str) -> dict:
judge_prompt = f"""Compare two responses to the given prompt.
## Prompt
{prompt}
## Response A
{response_a}
## Response B
{response_b}
## Criteria: {criteria}
Analyze both, then give verdict as "A", "B", or "tie".
JSON: {{"reasoning": "...", "verdict": "A" or "B" or "tie"}}"""
response = self.client.chat.completions.create(
model=self.model,
messages=[{"role": "user", "content": judge_prompt}],
response_format={"type": "json_object"},
temperature=0,
)
return json.loads(response.choices[0].message.content)
def score_response(self, prompt: str, response: str, rubric: dict) -> dict:
rubric_text = "\n".join(f"- {d}: " d, desc rubric.items())
judge_prompt =
resp = .client.chat.completions.create(
model=.model,
messages=[{: , : judge_prompt}],
response_format={: }, temperature=,
)
json.loads(resp.choices[].message.content)
Reducing Judge Bias
| Bias | Mitigation |
|---|
| Position bias | Randomize order, run both orderings |
| Verbosity bias | Include "conciseness" in criteria |
| Self-preference | Use different model as judge |
| Style bias | Focus rubric on content, not style |
def debiased_pairwise(judge, prompt, resp_a, resp_b, criteria) -> dict:
"""Run comparison in both orderings to reduce position bias."""
r1 = judge.pairwise_compare(prompt, resp_a, resp_b, criteria)
r2 = judge.pairwise_compare(prompt, resp_b, resp_a, criteria)
v1 = r1["verdict"]
v2 = {"A": "B", "B": "A", "tie": "tie"}[r2["verdict"]]
if v1 == v2:
return {"verdict": v1, "confidence": "high"}
elif "tie" in (v1, v2):
return {"verdict": v1 if v2 == "tie" else v2, "confidence": "medium"}
return {"verdict": "tie", "confidence": "low"}
Custom Metrics Design
from abc import ABC, abstractmethod
class EvalMetric(ABC):
@property
@abstractmethod
def name(self) -> str: pass
@abstractmethod
def compute(self, prediction: str, reference, context: dict = None) -> float: pass
class StructuredOutputAccuracy(EvalMetric):
@property
def name(self): return "structured_output_accuracy"
def compute(self, prediction: str, reference: dict, context=None) -> float:
import json
try:
parsed = json.loads(prediction)
except json.JSONDecodeError:
return 0.0
expected = set(reference.keys())
actual = set(parsed.keys())
key_score = len(expected & actual) / len(expected) if expected else 1.0
type_ok = sum(
k expected & actual
(parsed[k]).__name__ == (reference[k]).__name__
)
(key_score + type_ok / ((expected), )) /
():
():
() -> :
constraints = (context {}).get(, [])
constraints:
met =
c constraints:
c[] == (prediction.split()) <= c[]:
met +=
c[] == c[].lower() prediction.lower():
met +=
c[] == prediction.strip().startswith(c[]):
met +=
met / (constraints)
Human Evaluation Protocols
Absolute Rating:
- Rate each response 1-5 on multiple dimensions
- Min 3 annotators per item, include calibration examples
Pairwise Preference:
- Show two responses side by side (randomized order)
- Pick: A better / B better / tie
- Most reliable for model ranking
Best-of-N:
- Show N responses from N models, rank all
- Efficient for comparing multiple models
Inter-Annotator Agreement
from sklearn.metrics import cohen_kappa_score
import numpy as np
def compute_agreement(annotations: list[list[int]]) -> dict:
"""Compute pairwise Cohen's Kappa across annotators."""
kappas = []
for i in range(len(annotations)):
for j in range(i + 1, len(annotations)):
kappas.append(cohen_kappa_score(annotations[i], annotations[j]))
mean_k = np.mean(kappas)
level = ("almost perfect" if mean_k > 0.8 else "substantial" if mean_k > 0.6
else "moderate" if mean_k > 0.4 else "fair" if mean_k > 0.2 else "slight")
return {"mean_kappa": round(mean_k, 4), "agreement_level": level}
Evaluation Pipeline
from dataclasses import dataclass, field
from datetime import datetime
from pathlib import Path
import json, numpy as np
@dataclass
class EvalConfig:
model_name: str
eval_suite: str
tasks: list[str]
num_samples: int = None
seed: int = 42
class EvaluationPipeline:
def __init__(self, config: EvalConfig):
self.config = config
self.metrics: list[EvalMetric] = []
def add_metric(self, metric: EvalMetric):
self.metrics.append(metric)
def run(self, dataset: list[dict], model_fn) -> dict:
import random
random.seed(self.config.seed)
samples = dataset
if self.config.num_samples:
samples = random.sample(dataset, min(self.config.num_samples, len(dataset)))
per_sample = []
for s in samples:
pred = model_fn(s["prompt"])
scores = {m.name: m.compute(pred, s.get(), s.get(, {})) m .metrics}
per_sample.append(scores)
aggregated = {}
m .metrics:
vals = [r[m.name] r per_sample]
aggregated[m.name] = {: (np.mean(vals), ), : (np.std(vals), )}
{: (.config), : aggregated, : (samples)}
Leaderboard Pitfalls
| Pitfall | What to Check |
|---|
| Contamination | Training data cutoff, decontamination methods |
| Cherry-picking | Look for comprehensive evals, not single metrics |
| Prompt sensitivity | Check if few-shot format matches reported results |
| Stale benchmarks | Prefer recent benchmarks, check for data leaks |
| Missing error bars | Look for confidence intervals or repeated runs |
Evaluation Anti-Patterns
Evaluating on training data -> Hold out test data, check contamination
Single metric decisions -> Multi-dimensional evaluation
Ignoring variance -> Report confidence intervals, multiple seeds
Static evaluation -> Automate in CI/CD
Trusting LLM judges blindly -> Calibrate against human annotations
Benchmarks as product decisions -> Always validate on your specific data
Checklist
When to Use
Use this skill when:
- Designing or implementing model evaluation specialist solutions
- Reviewing or improving existing model evaluation specialist approaches
- Making architectural or implementation decisions about model evaluation specialist
- Learning model evaluation specialist patterns and best practices
- Troubleshooting model evaluation specialist-related issues
Do NOT use this skill when:
- The question is about a fundamentally different technology domain
- A more specific sibling skill covers the exact topic needed
- The user needs a complete hands-on tutorial rather than expert guidance
Output Format
# Model Evaluation Specialist Analysis
## Context Assessment
[Situation summary and constraints]
## Recommended Approach
[Primary recommendation with rationale]
## Implementation Steps
1. [Step with specific details]
2. [Step with specific details]
3. [Step with specific details]
## Trade-offs and Considerations
- [Key trade-off 1]
- [Key trade-off 2]
## Next Steps
- [Immediate action item]
- [Follow-up action item]
Example
Input: "Help me implement model evaluation specialist for a medium-scale production application"
Output: A structured analysis covering current state assessment, recommended model evaluation specialist approach with specific patterns, implementation roadmap with milestones, and risk mitigation strategies tailored to the application scale and constraints.
Edge Cases
- Legacy system integration: When model evaluation specialist must coexist with legacy approaches, provide a gradual migration path rather than a complete rewrite
- Scale mismatch: When the solution complexity exceeds the project scale, recommend a simpler approach and note when to revisit
- Team skill gaps: When the team lacks experience with the recommended approach, include learning resources and simpler alternatives
- Conflicting requirements: When constraints conflict (e.g., performance vs. maintainability), explicitly state the trade-off and recommend based on stated priorities
