Troubleshooting AWS Hallucinations from Vector Store DBs

Join us as Amelia shares the debugging story nobody tells you about - how her vector store DB couldn't surface specific data until she tested it with simplified data from ChatGPT. Amelia walks through her journey from throwing JIRA tickets into a large language model without understanding pipelines or data cleaning, to discovering why her production vector store was failing. You'll learn about the gap between chatting with data and getting accurate connections, how to validate vector similarity search results, the difference between production and synthetic test data, and practical troubleshooting workflows for AWS vector stores. This episode reveals the messy reality of RAG systems - when everything seems fine but the outputs are subtly wrong, and how testing with simplified data can expose what production complexity hides. Timestamps 0:00 Cold Open 1:03 Welcome & Introduction 2:06 Amelia's Background & DeepRacer Trophy 4:49 The JIRA Ticket Use Case Origin Story 5:53 Getting Into the Presentation 6:03 Accessing & Cleaning Data Sets 8:12 Losing Production Data & Recreating with ChatGPT 12:45 Understanding Vector Databases 18:22 How Embeddings Work 24:16 The Hallucination Discovery 30:41 Testing Strategies for Vector Stores 36:52 Debugging Vector Similarity Search 42:18 Real-World Troubleshooting Workflows 44:26 Where to Find Amelia & Wrap-up How to find Amelia:

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Troubleshooting AWS Hallucinations from Vector Store DBs is an episode from vBrownBag by Alastair Cooke.

This episode is 00:48:04 long.

This episode was published on Mar 5, 2026.

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