---
title: "Manual 06: Evaluation, Benchmarking & Observability — RAG Canon"
description: "Technical implementation manual for Evaluation, Benchmarking & Observability in the RAG Canon."
image: "https://tinycto.tv/assets/rag-canon/rag_manuals_og.jpg"
canonicalUrl: "https://tinycto.tv/rag-canon/manuals/06-evaluation-observability"
locale: "en"
---

# Engineering Manual 06: Evaluation, Benchmarking & Observability

## 1. The RAG Triad of Core Metrics
Evaluating RAG systems requires decoupling retrieval precision from generation fidelity. The canonical RAG triad comprises:
1. **Context Precision / Relevance:** Did the retrieval pipeline fetch passages directly relevant to the query?
2. **Groundedness / Faithfulness:** Is every claim in the generated response mathematically or logically supported by the retrieved context?
3. **Answer Relevance:** Does the response directly address the user query without irrelevant tangents?

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## 2. Rigorous Evaluation Methodologies
- **Prediction-Powered Inference (PPI):** Combining a small set of human ground-truth labels with machine LLM judge predictions to compute statistically sound confidence intervals.
- **Synthetic Test Generation:** Automatically synthesizing multi-hop questions, unanswerable queries, and adversarial distractors from source corpuses to stress-test pipelines.
- **Distributed Telemetry:** Instrumenting every pipeline component with OpenTelemetry tracing spans, capturing end-to-end latency, token consumption, and retrieval scores.