---
title: "Discussion on \"Evaluating your RAG pipeline\" | Hashnode"
description: "Discussion on \"Evaluating your RAG pipeline\". Error analysis, golden datasets, context recall, and faithfulness."
url: "https://hashnode.com/posts/rag-evaluation-deepeval/6acda36567b018bc3e2322a6"
---

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# [Evaluating your RAG pipeline](https://krishparekh.hashnode.dev/rag-evaluation-deepeval?utm_source=hashnode&utm_medium=feed)

Error analysis, golden datasets, context recall, and faithfulness.

Aug 4 · 16 min read · krishparekh.hashnode.dev  
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-   [#llm](https://hashnode.com/tag/llm)
-   [#ai](https://hashnode.com/tag/ai)
-   [#testing](https://hashnode.com/tag/testing)
-   [#machine-learning](https://hashnode.com/tag/machine-learning)

## Written by

[Krish Parekh](https://hashnode.com/@KrishParekh)

@KrishParekh · Melbourne, Australia

  
[View profile](https://hashnode.com/@KrishParekh)

AI Engineer at SMEC AI · RAG, agents and voice AI

I build RAG systems, multi-agent applications and voice agents, along with the APIs and cloud infrastructure behind them. Right now that means production voice agents and ad-creative pipelines for Australian businesses. My work spans Python, FastAPI, Amazon Bedrock and Terraform, alongside Next.js and TypeScript. I write Depth First, a newsletter on production AI engineering: guardrails, evaluation, LLM gateways and retrieval.

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