RAG Pipeline Evaluation¶

This page outlines how to evaluate and optimize the performance of the Obelisk RAG system.

Evaluation Metrics¶

The RAG pipeline will be evaluated using several key metrics:

Retrieval Metrics¶

• Precision@k: Proportion of relevant documents in the top k results
• Recall@k: Proportion of all relevant documents that appear in the top k
• Mean Reciprocal Rank (MRR): Average position of the first relevant document
• Normalized Discounted Cumulative Gain (NDCG): Measures ranking quality

Generation Metrics¶

• Answer Relevance: How relevant the answer is to the question
• Factual Correctness: Whether the answer contains factual errors
• Hallucination Rate: Proportion of generated content not supported by context
• Citation Accuracy: Whether sources are accurately cited
• Completeness: Whether the answer fully addresses the question

System Metrics¶

• Latency: End-to-end response time
• Token Efficiency: Number of tokens used vs. information conveyed
• Resource Usage: Memory and CPU consumption
• Throughput: Queries processed per second

Evaluation Framework¶

The RAG pipeline will include a built-in evaluation framework:

# Future implementation example
class RAGEvaluator:
    def __init__(self, config):
        self.config = config
        self.test_cases = self._load_test_cases()

    def _load_test_cases(self):
        """Load test cases from configuration."""
        # Implementation details

    def evaluate_retrieval(self, query_processor):
        """Evaluate retrieval performance."""
        results = {}

        for test_case in self.test_cases:
            query = test_case["query"]
            relevant_docs = test_case["relevant_docs"]

            retrieved = query_processor.process_query(query)
            retrieved_ids = [doc["id"] for doc in retrieved["retrieved_chunks"]]

            results[query] = {
                "precision": self._calculate_precision(retrieved_ids, relevant_docs),
                "recall": self._calculate_recall(retrieved_ids, relevant_docs),
                "mrr": self._calculate_mrr(retrieved_ids, relevant_docs),
                "ndcg": self._calculate_ndcg(retrieved_ids, relevant_docs)
            }

        return results

    def evaluate_generation(self, rag_pipeline):
        """Evaluate generation quality."""
        # Implementation details

    def evaluate_system(self, rag_pipeline):
        """Evaluate system performance."""
        # Implementation details

    def generate_report(self, results):
        """Generate evaluation report."""
        # Implementation details

Synthetic Test Suite¶

The evaluation framework will include a synthetic test suite:

1. Query Generation: Generate realistic user queries
2. Expected Answer Creation: Create expected answers
3. Document Tagging: Tag documents for relevance
4. Test Case Assembly: Create complete test cases

Example test case:

{
  "query": "How do I configure the Ollama service in Docker?",
  "query_type": "how-to",
  "relevant_docs": [
    "development/docker.md#ollama-service",
    "chatbot/index.md#services-configuration"
  ],
  "expected_answer_elements": [
    "Ollama service configuration in docker-compose.yaml",
    "Environment variables for GPU acceleration",
    "Volume mounts for model storage"
  ],
  "difficulty": "medium"
}

Human Evaluation¶

In addition to automated metrics, human evaluation will be critical:

1. Side-by-side comparisons: Compare RAG vs. non-RAG responses
2. Blind evaluation: Rate responses without knowing the source
3. Expert review: Domain experts evaluate factual correctness
4. User feedback collection: Gather feedback from real users

Evaluation Dashboard¶

The RAG pipeline will include a visual dashboard for evaluation:

graph TD
    A[Evaluation Runner] -->|Executes Tests| B[Test Suite]
    B -->|Generates Metrics| C[Metrics Store]
    C -->|Visualizes Results| D[Dashboard]
    D -->|Precision| E[Retrieval Metrics]
    D -->|Correctness| F[Generation Metrics]
    D -->|Performance| G[System Metrics]
    D -->|Overall| H[Combined Score]

Continuous Improvement¶

The evaluation system will enable continuous improvement:

Error Analysis¶

Categorizing and tracking error types:

• Retrieval failures: Relevant content not retrieved
• Context utilization errors: Context ignored or misinterpreted
• Hallucination instances: Information not grounded in context
• Citation errors: Missing or incorrect citations

Optimization Process¶

A systematic approach to RAG optimization:

1. Baseline establishment: Measure initial performance
2. Component isolation: Test each component independently
3. Ablation studies: Remove components to measure impact
4. Parameter tuning: Optimize configuration parameters
5. A/B testing: Compare variations with real users

Implementation Roadmap¶

The evaluation system will be implemented in phases:

Best Practices¶

Recommendations for effective RAG evaluation:

1. Diverse test cases: Include varied query types and difficulty levels
2. Regular re-evaluation: Test after each significant change
3. User-focused metrics: Prioritize metrics aligned with user satisfaction
4. Documentation-specific evaluation: Create tests specific to documentation use cases
5. Comparative analysis: Benchmark against similar systems