# Enterprise Research Model Evaluation Framework

Category: Retrieval & Pipelines
Stack: MLflow, DeepEval, Golden Datasets

## Overview
Benchmarking commercial and open-weight models across government policy corpora with deterministic factual assertions.

## The Problem
Comparing language models using qualitative chat impressions fails to catch subtle hallucinations, reasoning drift, and citation errors.

## Production Rule
Build an automated evaluation suite with at least 50 golden question-answer pairs. Score candidate models on schema validity, source attribution, and numeric faithfulness.

## Code / Specification
File: `eval_framework.py`

```evaluation suite
def evaluate_model_turn(prediction: str, golden_record: dict) -> dict:
    has_valid_citations = all(c in prediction for c in golden_record['expected_citations'])
    exact_match_score = 1.0 if golden_record['target_answer'] in prediction else 0.0
    
    return {
        'citation_precision': 1.0 if has_valid_citations else 0.0,
        'exact_match': exact_match_score,
        'passed': has_valid_citations and (exact_match_score > 0.8)
    }
```