---
title: "Step 8: Evaluation Metrics - Measuring and Improving RAG Quality"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Step 8: Evaluation Metrics - Measuring and Improving RAG Quality

## Learning Objectives
By the end of this module, you will be able to:
- Implement comprehensive RAG evaluation frameworks
- Use RAGAS for automated quality assessment
- Design custom metrics for specific use cases
- Build A/B testing systems for RAG improvements
- Create feedback loops for continuous optimization

## Theoretical Foundation

### Why RAG Evaluation is Critical

**The Challenge:**
- **Subjective quality**: "Good" answers vary by user and context
- **Multiple failure modes**: Poor retrieval, bad generation, or both
- **Scale complexity**: Hard to manually evaluate thousands of queries
- **Optimization targets**: Need metrics to guide improvements

**Evaluation Dimensions:**
- **Retrieval Quality**: Are relevant documents found?
- **Generation Quality**: Are answers accurate and helpful?
- **User Experience**: Are responses fast and satisfying?
- **System Reliability**: Does it work consistently?

### RAG Evaluation Framework

**Component-Level Evaluation:**
1. **Retrieval Metrics**: Precision, Recall, MRR, NDCG
2. **Generation Metrics**: Faithfulness, Relevance, Coherence
3. **End-to-End Metrics**: Answer accuracy, User satisfaction
4. **System Metrics**: Latency, Throughput, Error rates

## RAGAS: RAG Assessment Framework

### RAGAS Overview

**What is RAGAS:**
- **Purpose**: Automated evaluation framework for RAG systems
- **Metrics**: Pre-built metrics for common RAG evaluation tasks
- **LLM-based**: Uses language models to assess quality
- **Comprehensive**: Covers both retrieval and generation aspects

**Key RAGAS Metrics:**

### 1. Faithfulness
**Measures**: How well the answer is grounded in retrieved context
**Formula**: Number of faithful statements / Total statements
```python
from ragas.metrics import faithfulness
from ragas import evaluate

# Evaluate faithfulness
faithfulness_score = evaluate(
    dataset=evaluation_dataset,
    metrics=[faithfulness]
)

print(f"Faithfulness Score: {faithfulness_score['faithfulness']}")
```

### 2. Answer Relevancy
**Measures**: How relevant the answer is to the question
**Method**: Generates questions from answer, compares to original
```python
from ragas.metrics import answer_relevancy

relevancy_score = evaluate(
    dataset=evaluation_dataset,
    metrics=[answer_relevancy]
)
```

### 3. Context Precision
**Measures**: How relevant retrieved contexts are to the question
**Focus**: Quality of retrieval system
```python
from ragas.metrics import context_precision

precision_score = evaluate(
    dataset=evaluation_dataset,
    metrics=[context_precision]
)
```

### 4. Context Recall
**Measures**: How much relevant information was retrieved
**Focus**: Completeness of retrieval
```python
from ragas.metrics import context_recall

recall_score = evaluate(
    dataset=evaluation_dataset,
    metrics=[context_recall]
)
```

### RAGAS Implementation

**1. Dataset Preparation**
```python
from datasets import Dataset
import pandas as pd

def prepare_ragas_dataset(
    questions: List[str],
    ground_truth_answers: List[str],
    rag_answers: List[str],
    retrieved_contexts: List[List[str]]
) -> Dataset:
    """Prepare dataset for RAGAS evaluation"""
    
    # Create evaluation dataset
    eval_data = {
        "question": questions,
        "answer": rag_answers,
        "contexts": retrieved_contexts,
        "ground_truth": ground_truth_answers
    }
    
    # Convert to HuggingFace Dataset
    dataset = Dataset.from_dict(eval_data)
    
    return dataset

# Example usage
evaluation_dataset = prepare_ragas_dataset(
    questions=[
        "How do I implement OAuth2 in Python?",
        "What are the benefits of vector databases?",
        "How to handle authentication errors?"
    ],
    ground_truth_answers=[
        "OAuth2 can be implemented in Python using libraries like...",
        "Vector databases provide fast similarity search...",
        "Authentication errors should be handled by..."
    ],
    rag_answers=[
        # Your RAG system's answers
        get_rag_answer("How do I implement OAuth2 in Python?"),
        get_rag_answer("What are the benefits of vector databases?"),
        get_rag_answer("How to handle authentication errors?")
    ],
    retrieved_contexts=[
        # Retrieved contexts for each question
        get_retrieved_contexts("How do I implement OAuth2 in Python?"),
        get_retrieved_contexts("What are the benefits of vector databases?"),
        get_retrieved_contexts("How to handle authentication errors?")
    ]
)
```

**2. Comprehensive RAGAS Evaluation**
```python
from ragas.metrics import (
    faithfulness,
    answer_relevancy,
    context_precision,
    context_recall,
    context_relevancy
)

def comprehensive_ragas_evaluation(dataset: Dataset) -> Dict[str, float]:
    """Run complete RAGAS evaluation suite"""
    
    # Define metrics to evaluate
    metrics = [
        faithfulness,
        answer_relevancy,
        context_precision,
        context_recall,
        context_relevancy
    ]
    
    # Run evaluation
    results = evaluate(
        dataset=dataset,
        metrics=metrics,
        llm=get_evaluation_llm(),  # LLM for evaluation
        embeddings=get_evaluation_embeddings()  # Embeddings for similarity
    )
    
    return results

# Run evaluation
evaluation_results = comprehensive_ragas_evaluation(evaluation_dataset)

# Print results
for metric, score in evaluation_results.items():
    print(f"{metric}: {score:.3f}")
```

## Custom Evaluation Metrics

### Domain-Specific Metrics

**1. Technical Accuracy Metric**
```python
class TechnicalAccuracyMetric:
    def __init__(self, domain_knowledge_base: Dict[str, str]):
        self.knowledge_base = domain_knowledge_base
        self.technical_terms = load_technical_terms(domain_knowledge_base)
    
    def evaluate(self, question: str, answer: str, context: str) -> float:
        """Evaluate technical accuracy of the answer"""
        
        # Extract technical terms from answer
        answer_terms = extract_technical_terms(answer)
        
        # Check accuracy of each term
        accurate_terms = 0
        total_terms = len(answer_terms)
        
        for term in answer_terms:
            if self.is_technically_accurate(term, context):
                accurate_terms += 1
        
        return accurate_terms / total_terms if total_terms > 0 else 1.0
    
    def is_technically_accurate(self, term: str, context: str) -> bool:
        """Check if technical term usage is accurate"""
        
        # Look up correct definition
        correct_definition = self.knowledge_base.get(term.lower())
        
        if not correct_definition:
            return True  # Unknown term, assume correct
        
        # Check if context supports correct usage
        context_similarity = calculate_similarity(correct_definition, context)
        return context_similarity > 0.7
```

**2. Completeness Metric**
```python
class CompletenessMetric:
    def __init__(self):
        self.key_aspects_extractor = KeyAspectsExtractor()
    
    def evaluate(self, question: str, answer: str, ground_truth: str) -> float:
        """Measure how completely the question is answered"""
        
        # Extract key aspects that should be covered
        required_aspects = self.key_aspects_extractor.extract(question, ground_truth)
        
        # Check which aspects are covered in the answer
        covered_aspects = []
        for aspect in required_aspects:
            if self.is_aspect_covered(aspect, answer):
                covered_aspects.append(aspect)
        
        completeness_score = len(covered_aspects) / len(required_aspects)
        
        return completeness_score
    
    def is_aspect_covered(self, aspect: str, answer: str) -> bool:
        """Check if a key aspect is covered in the answer"""
        
        # Use semantic similarity to check coverage
        aspect_embedding = embed_text(aspect)
        answer_sentences = split_into_sentences(answer)
        
        for sentence in answer_sentences:
            sentence_embedding = embed_text(sentence)
            similarity = cosine_similarity(aspect_embedding, sentence_embedding)
            
            if similarity > 0.6:  # Threshold for coverage
                return True
        
        return False
```

### Retrieval-Specific Metrics

**1. Advanced Precision@K**
```python
def precision_at_k(
    retrieved_docs: List[str],
    relevant_docs: List[str],
    k: int
) -> float:
    """Calculate precision at k with different relevance levels"""
    
    if k == 0 or len(retrieved_docs) == 0:
        return 0.0
    
    top_k_retrieved = retrieved_docs[:k]
    relevant_in_top_k = sum(1 for doc in top_k_retrieved if doc in relevant_docs)
    
    return relevant_in_top_k / k

def weighted_precision_at_k(
    retrieved_docs: List[str],
    doc_relevance_scores: Dict[str, float],
    k: int
) -> float:
    """Precision@K with weighted relevance scores"""
    
    if k == 0 or len(retrieved_docs) == 0:
        return 0.0
    
    top_k_retrieved = retrieved_docs[:k]
    total_relevance = sum(
        doc_relevance_scores.get(doc, 0.0) 
        for doc in top_k_retrieved
    )
    
    return total_relevance / k
```

**2. Mean Reciprocal Rank (MRR)**
```python
def mean_reciprocal_rank(
    queries_and_results: List[Tuple[str, List[str], List[str]]]
) -> float:
    """Calculate MRR across multiple queries"""
    
    reciprocal_ranks = []
    
    for query, retrieved_docs, relevant_docs in queries_and_results:
        # Find rank of first relevant document
        first_relevant_rank = None
        
        for rank, doc in enumerate(retrieved_docs, 1):
            if doc in relevant_docs:
                first_relevant_rank = rank
                break
        
        if first_relevant_rank:
            reciprocal_ranks.append(1.0 / first_relevant_rank)
        else:
            reciprocal_ranks.append(0.0)
    
    return sum(reciprocal_ranks) / len(reciprocal_ranks)
```

**3. Normalized Discounted Cumulative Gain (NDCG)**
```python
def ndcg_at_k(
    retrieved_docs: List[str],
    relevance_scores: Dict[str, float],
    k: int
) -> float:
    """Calculate NDCG@K for ranked results"""
    
    def dcg_at_k(docs: List[str], scores: Dict[str, float], k: int) -> float:
        """Calculate DCG@K"""
        dcg = 0.0
        for i, doc in enumerate(docs[:k]):
            relevance = scores.get(doc, 0.0)
            dcg += relevance / math.log2(i + 2)  # i+2 because log2(1) = 0
        return dcg
    
    # Calculate DCG for retrieved results
    dcg = dcg_at_k(retrieved_docs, relevance_scores, k)
    
    # Calculate IDCG (Ideal DCG)
    ideal_docs = sorted(
        relevance_scores.keys(),
        key=lambda x: relevance_scores[x],
        reverse=True
    )
    idcg = dcg_at_k(ideal_docs, relevance_scores, k)
    
    return dcg / idcg if idcg > 0 else 0.0
```

## A/B Testing Framework

### Experiment Design

**1. RAG System Comparison**
```python
class RAGABTest:
    def __init__(self, system_a: RAGSystem, system_b: RAGSystem):
        self.system_a = system_a
        self.system_b = system_b
        self.results = []
    
    def run_experiment(
        self,
        test_queries: List[str],
        evaluation_metrics: List[Callable],
        sample_size_per_group: int = 100
    ) -> Dict[str, Any]:
        """Run A/B test comparing two RAG systems"""
        
        # Randomly assign queries to groups
        queries_a = random.sample(test_queries, sample_size_per_group)
        queries_b = random.sample(
            [q for q in test_queries if q not in queries_a],
            sample_size_per_group
        )
        
        # Collect results from both systems
        results_a = self.evaluate_system(self.system_a, queries_a, evaluation_metrics)
        results_b = self.evaluate_system(self.system_b, queries_b, evaluation_metrics)
        
        # Statistical analysis
        statistical_results = self.statistical_analysis(results_a, results_b)
        
        return {
            "system_a_results": results_a,
            "system_b_results": results_b,
            "statistical_analysis": statistical_results,
            "winner": self.determine_winner(statistical_results)
        }
    
    def evaluate_system(
        self,
        system: RAGSystem,
        queries: List[str],
        metrics: List[Callable]
    ) -> Dict[str, List[float]]:
        """Evaluate a RAG system on given queries"""
        
        metric_scores = {metric.__name__: [] for metric in metrics}
        
        for query in queries:
            # Get system response
            response = system.query(query)
            
            # Calculate metrics
            for metric in metrics:
                score = metric(query, response.answer, response.context)
                metric_scores[metric.__name__].append(score)
        
        return metric_scores
    
    def statistical_analysis(
        self,
        results_a: Dict[str, List[float]],
        results_b: Dict[str, List[float]]
    ) -> Dict[str, Dict[str, float]]:
        """Perform statistical analysis of A/B test results"""
        
        from scipy import stats
        
        analysis = {}
        
        for metric_name in results_a.keys():
            scores_a = results_a[metric_name]
            scores_b = results_b[metric_name]
            
            # T-test for significant difference
            t_stat, p_value = stats.ttest_ind(scores_a, scores_b)
            
            # Effect size (Cohen's d)
            pooled_std = math.sqrt(
                ((len(scores_a) - 1) * np.var(scores_a, ddof=1) +
                 (len(scores_b) - 1) * np.var(scores_b, ddof=1)) /
                (len(scores_a) + len(scores_b) - 2)
            )
            cohens_d = (np.mean(scores_a) - np.mean(scores_b)) / pooled_std
            
            analysis[metric_name] = {
                "mean_a": np.mean(scores_a),
                "mean_b": np.mean(scores_b),
                "std_a": np.std(scores_a),
                "std_b": np.std(scores_b),
                "t_statistic": t_stat,
                "p_value": p_value,
                "effect_size": cohens_d,
                "significant": p_value < 0.05
            }
        
        return analysis
```

### Online Evaluation System

**2. Continuous A/B Testing**
```python
class OnlineEvaluationSystem:
    def __init__(self):
        self.experiment_configs = {}
        self.user_assignments = {}
        self.experiment_results = defaultdict(list)
    
    def create_experiment(
        self,
        experiment_id: str,
        system_variants: Dict[str, RAGSystem],
        traffic_split: Dict[str, float],
        success_metrics: List[str]
    ):
        """Create a new online experiment"""
        
        self.experiment_configs[experiment_id] = {
            "variants": system_variants,
            "traffic_split": traffic_split,
            "metrics": success_metrics,
            "start_time": datetime.now(),
            "status": "active"
        }
    
    def assign_user_to_variant(
        self,
        user_id: str,
        experiment_id: str
    ) -> str:
        """Assign user to experiment variant"""
        
        if user_id in self.user_assignments.get(experiment_id, {}):
            return self.user_assignments[experiment_id][user_id]
        
        # Random assignment based on traffic split
        config = self.experiment_configs[experiment_id]
        variant = self.weighted_random_choice(config["traffic_split"])
        
        if experiment_id not in self.user_assignments:
            self.user_assignments[experiment_id] = {}
        
        self.user_assignments[experiment_id][user_id] = variant
        return variant
    
    def log_interaction(
        self,
        user_id: str,
        experiment_id: str,
        query: str,
        response: RAGResponse,
        feedback: Dict[str, Any]
    ):
        """Log user interaction for experiment analysis"""
        
        variant = self.user_assignments[experiment_id][user_id]
        
        interaction_data = {
            "user_id": user_id,
            "experiment_id": experiment_id,
            "variant": variant,
            "query": query,
            "response": response,
            "feedback": feedback,
            "timestamp": datetime.now()
        }
        
        self.experiment_results[experiment_id].append(interaction_data)
    
    def analyze_experiment(
        self,
        experiment_id: str,
        min_samples: int = 100
    ) -> Dict[str, Any]:
        """Analyze experiment results"""
        
        results = self.experiment_results[experiment_id]
        
        if len(results) < min_samples:
            return {"status": "insufficient_data", "sample_size": len(results)}
        
        # Group by variant
        variant_results = defaultdict(list)
        for result in results:
            variant_results[result["variant"]].append(result)
        
        # Calculate metrics for each variant
        variant_metrics = {}
        for variant, variant_data in variant_results.items():
            variant_metrics[variant] = self.calculate_variant_metrics(variant_data)
        
        # Statistical significance testing
        significance_tests = self.run_significance_tests(variant_metrics)
        
        return {
            "experiment_id": experiment_id,
            "sample_size": len(results),
            "variant_metrics": variant_metrics,
            "significance_tests": significance_tests,
            "recommendation": self.generate_recommendation(significance_tests)
        }
```

## Feedback Loop Implementation

### User Feedback Collection

**1. Implicit Feedback**
```python
class ImplicitFeedbackCollector:
    def __init__(self):
        self.interaction_tracker = InteractionTracker()
    
    def track_user_behavior(
        self,
        user_id: str,
        query: str,
        results: List[SearchResult],
        interactions: List[UserInteraction]
    ) -> FeedbackSignals:
        """Extract implicit feedback from user behavior"""
        
        signals = FeedbackSignals()
        
        # Click-through rate
        clicked_results = [i for i in interactions if i.type == "click"]
        signals.ctr = len(clicked_results) / len(results) if results else 0
        
        # Dwell time (time spent reading results)
        dwell_times = [i.duration for i in interactions if i.type == "dwell"]
        signals.avg_dwell_time = np.mean(dwell_times) if dwell_times else 0
        
        # Result ranking satisfaction
        if clicked_results:
            first_click_position = min([i.position for i in clicked_results])
            signals.first_click_position = first_click_position
        
        # Query abandonment
        signals.abandoned = len(interactions) == 0
        
        # Follow-up queries
        follow_up_queries = self.detect_follow_up_queries(user_id, query)
        signals.needs_refinement = len(follow_up_queries) > 0
        
        return signals
```

**2. Explicit Feedback**
```python
class ExplicitFeedbackCollector:
    def __init__(self):
        self.feedback_store = FeedbackStore()
    
    def collect_rating_feedback(
        self,
        user_id: str,
        query: str,
        answer: str,
        rating: int,  # 1-5 scale
        feedback_text: Optional[str] = None
    ):
        """Collect explicit rating feedback"""
        
        feedback = ExplicitFeedback(
            user_id=user_id,
            query=query,
            answer=answer,
            rating=rating,
            feedback_text=feedback_text,
            timestamp=datetime.now()
        )
        
        self.feedback_store.save(feedback)
    
    def collect_relevance_feedback(
        self,
        user_id: str,
        query: str,
        results: List[SearchResult],
        relevance_labels: List[str]  # ["relevant", "irrelevant", "partially_relevant"]
    ):
        """Collect relevance feedback for search results"""
        
        for result, label in zip(results, relevance_labels):
            relevance_feedback = RelevanceFeedback(
                user_id=user_id,
                query=query,
                document_id=result.document_id,
                relevance_label=label,
                timestamp=datetime.now()
            )
            
            self.feedback_store.save(relevance_feedback)
```

### Continuous Improvement Loop

**3. Feedback Integration System**
```python
class ContinuousImprovementSystem:
    def __init__(self, rag_system: RAGSystem):
        self.rag_system = rag_system
        self.feedback_analyzer = FeedbackAnalyzer()
        self.model_updater = ModelUpdater()
    
    def analyze_feedback_patterns(self) -> List[ImprovementOpportunity]:
        """Analyze feedback to identify improvement opportunities"""
        
        # Collect recent feedback
        recent_feedback = self.feedback_store.get_recent_feedback(days=7)
        
        opportunities = []
        
        # Identify low-performing queries
        low_rated_queries = self.feedback_analyzer.find_low_rated_queries(
            recent_feedback, 
            threshold=3.0
        )
        
        for query_pattern in low_rated_queries:
            opportunities.append(ImprovementOpportunity(
                type="query_performance",
                description=f"Query pattern '{query_pattern}' consistently rated low",
                suggested_actions=["improve_retrieval", "update_documents", "query_expansion"]
            ))
        
        # Identify missing information
        abandoned_queries = self.feedback_analyzer.find_abandoned_queries(recent_feedback)
        
        for query in abandoned_queries:
            opportunities.append(ImprovementOpportunity(
                type="missing_information",
                description=f"Query '{query}' often abandoned - likely missing relevant docs",
                suggested_actions=["add_documents", "improve_indexing"]
            ))
        
        return opportunities
    
    def implement_improvements(
        self, 
        opportunities: List[ImprovementOpportunity]
    ) -> Dict[str, Any]:
        """Implement identified improvements"""
        
        improvement_results = {}
        
        for opportunity in opportunities:
            if "improve_retrieval" in opportunity.suggested_actions:
                # Retrain retrieval model with feedback data
                improvement_results["retrieval_retraining"] = (
                    self.retrain_retrieval_model(opportunity)
                )
            
            if "query_expansion" in opportunity.suggested_actions:
                # Add query expansion rules
                improvement_results["query_expansion"] = (
                    self.add_query_expansion_rules(opportunity)
                )
            
            if "add_documents" in opportunity.suggested_actions:
                # Suggest documents to add
                improvement_results["document_suggestions"] = (
                    self.suggest_missing_documents(opportunity)
                )
        
        return improvement_results
    
    def retrain_retrieval_model(
        self, 
        opportunity: ImprovementOpportunity
    ) -> Dict[str, Any]:
        """Retrain retrieval model using feedback data"""
        
        # Collect training data from feedback
        training_data = self.feedback_analyzer.extract_training_data(
            opportunity.description
        )
        
        # Retrain or fine-tune retrieval model
        retraining_results = self.model_updater.retrain_retrieval(training_data)
        
        # Update the live system
        if retraining_results["improvement_score"] > 0.05:  # 5% improvement threshold
            self.rag_system.update_retrieval_model(retraining_results["model"])
            return {"status": "deployed", "improvement": retraining_results["improvement_score"]}
        else:
            return {"status": "insufficient_improvement", "improvement": retraining_results["improvement_score"]}
```

## Evaluation Dashboard and Monitoring

### Real-time Monitoring System
```python
class RAGMonitoringDashboard:
    def __init__(self):
        self.metrics_collector = MetricsCollector()
        self.alert_system = AlertSystem()
    
    def get_real_time_metrics(self) -> Dict[str, Any]:
        """Get current system performance metrics"""
        
        current_metrics = {
            "response_time": {
                "avg_ms": self.metrics_collector.get_avg_response_time(),
                "p95_ms": self.metrics_collector.get_p95_response_time(),
                "p99_ms": self.metrics_collector.get_p99_response_time()
            },
            "quality_metrics": {
                "avg_rating": self.metrics_collector.get_avg_user_rating(),
                "success_rate": self.metrics_collector.get_success_rate(),
                "abandonment_rate": self.metrics_collector.get_abandonment_rate()
            },
            "system_health": {
                "error_rate": self.metrics_collector.get_error_rate(),
                "uptime": self.metrics_collector.get_uptime(),
                "index_freshness": self.metrics_collector.get_index_freshness()
            }
        }
        
        # Check for alerts
        alerts = self.alert_system.check_alerts(current_metrics)
        current_metrics["alerts"] = alerts
        
        return current_metrics
    
    def generate_evaluation_report(
        self, 
        time_period: str = "last_7_days"
    ) -> Dict[str, Any]:
        """Generate comprehensive evaluation report"""
        
        # Collect data for time period
        evaluation_data = self.metrics_collector.get_period_data(time_period)
        
        # Run RAGAS evaluation on sample
        sample_queries = self.select_evaluation_sample(evaluation_data)
        ragas_results = self.run_ragas_evaluation(sample_queries)
        
        # Custom metrics
        custom_metrics = self.calculate_custom_metrics(evaluation_data)
        
        # Trend analysis
        trends = self.analyze_trends(evaluation_data)
        
        return {
            "period": time_period,
            "ragas_metrics": ragas_results,
            "custom_metrics": custom_metrics,
            "trends": trends,
            "recommendations": self.generate_recommendations(ragas_results, trends)
        }
```

## What We'll Build

### Complete Evaluation System
```python
class ComprehensiveRAGEvaluator:
    def __init__(self, rag_system: RAGSystem):
        self.rag_system = rag_system
        self.ragas_evaluator = RAGASEvaluator()
        self.custom_metrics = CustomMetricsSuite()
        self.ab_tester = RAGABTest()
        self.feedback_system = FeedbackSystem()
        self.improvement_loop = ContinuousImprovementSystem(rag_system)
    
    def run_comprehensive_evaluation(
        self,
        test_dataset: Dataset,
        evaluation_config: EvaluationConfig
    ) -> EvaluationReport:
        """Run complete evaluation suite"""
        
        # RAGAS evaluation
        ragas_results = self.ragas_evaluator.evaluate(test_dataset)
        
        # Custom metrics
        custom_results = self.custom_metrics.evaluate(test_dataset)
        
        # Performance metrics
        performance_results = self.measure_performance(test_dataset)
        
        # Generate report
        report = EvaluationReport(
            ragas_metrics=ragas_results,
            custom_metrics=custom_results,
            performance_metrics=performance_results,
            overall_score=self.calculate_overall_score(
                ragas_results, custom_results, performance_results
            ),
            recommendations=self.generate_improvement_recommendations()
        )
        
        return report
```

### Features to Implement
1. **RAGAS integration**: Automated quality assessment
2. **Custom metrics**: Domain-specific evaluation measures
3. **A/B testing**: System comparison and optimization
4. **Feedback loops**: Continuous improvement from user data
5. **Monitoring dashboard**: Real-time performance tracking

## Success Criteria
- Implement working RAGAS evaluation pipeline
- Create custom metrics for specific use cases
- Build A/B testing system for comparing RAG approaches
- Establish feedback collection and improvement loops
- Deploy monitoring system that tracks quality over time

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**Next Step**: In Step 9, we'll explore RAGFlow platform integration, which provides a comprehensive UI and workflow management system for production RAG deployments.
