Building an Automated Evaluation Harness
Create a repeatable evaluation pipeline that runs your full RAG system against a test set, computes all metrics, and generates a report so you can track improvements over time.
What Is an Evaluation Harness?
An evaluation harness is a repeatable, automated pipeline that runs your full RAG system against a standardized test set, computes all metrics, and produces a report. The key word is repeatable: every time you make a change to your chunking strategy, embedding model, prompt, or LLM, you run the same harness and compare results against a baseline. This transforms RAG development from subjective tinkering into data-driven engineering.
Harness Architecture
A well-designed evaluation harness has four layers: Test data management (load and version the golden dataset), pipeline execution (run each test question through the full RAG pipeline), metrics computation (calculate all retrieval and generation metrics), and report generation (save results with version info and produce a diff against the previous baseline). Each layer should be independently testable and configurable.
class RAGEvaluationHarness:
def __init__(self, retriever, llm_client, config):
self.retriever = retriever
self.llm_client = llm_client
self.config = config # chunk_size, top_k, model, threshold, etc.
self.results = []
def run(self, golden_dataset):
for item in golden_dataset:
result = self._evaluate_single(item)
self.results.append(result)
metrics = self._compute_metrics()
self._save_report(metrics)
return metricsAll lessons in this course
- Why Evaluation Matters in RAG
- Retrieval Metrics: Hit Rate, MRR, and NDCG
- Generation Metrics: Faithfulness and Answer Relevance
- Building an Automated Evaluation Harness