Learn GPT

Building RAG

Unit 1

Langraph Introduction

Overview of Langraph and Retrieval-Augmented Generation

Key Concepts and Architecture of Langgraph

Installing and Configuring Langgraph Environment

Basic Operations and First Example in Langgraph

Unit 2

Example Building Tutorial

Designing a Complete RAG Pipeline with Langgraph

Data Ingestion, Chunking and Vector Store Integration

Building Retrieval and Generation Nodes in Langgraph

Testing, Debugging and Evaluating the RAG System

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Unit 2 • Chapter 4

Testing, Debugging and Evaluating the RAG System

Summary

The video explains methods for testing, debugging, and evaluating Retrieval‑Augmented Generation (RAG) pipelines. It covers unit tests for retrievers and generators, integration tests that verify end‑to‑end behavior, and techniques for tracing token flow. Debugging strategies include inspecting retrieved documents, using synthetic queries, and logging LLM prompts. Evaluation metrics discussed are relevance (nDCG, MAP), factual correctness (faithfulness scores), and end‑user usefulness (human rating). The speaker emphasizes iterative cycles: test components, debug failures, measure performance, and refine prompts or index structures. Tools such as LangChain’s test harness, Haystack’s evaluation suite, and custom scripts are demonstrated. Best practices include separating data preparation from model inference, mocking external APIs during tests, and maintaining versioned evaluation datasets. The video concludes with a checklist for robust RAG development.

Concept Check

Which metric specifically measures factual consistency between a generated answer and its source documents?

Perplexity

ROUGE

BLEU

Faithfulness score

In unit testing a RAG retriever, which mock object is most appropriate?

Mock tokenizer splitting words

Mock HTTP client for API calls

Mock LLM generating random text

Mock vector store returning predefined embeddings

What is a primary advantage of using synthetic queries during debugging?

Improve model training speed

Increase dataset size

Reduce compute cost

Expose edge cases not covered by real queries

Which tool provides a built-in evaluation suite for Haystack RAG pipelines?

Haystack's eval module

LangChain test harness

OpenAI Playground

Transformers Trainer

When performing integration testing of a RAG system, what should be validated?

Only LLM token count

Only retriever speed

End‑to‑end answer relevance and source attribution

Only index size

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