A field report from rolling out retrieval-augmented generation in production, including cache bugs, bad embeddings, and how we fixed them.
When we first rolled out a RAG-based assistant for our internal SRE team, nothing in the vendor docs really prepared us for the messy parts.
The first painful incident happened on a Monday morning. A runbook query returned an outdated PostgreSQL failover procedure because:
Two weeks later, we saw a spike in “no relevant context found” errors during incident calls. The vector DB was healthy; the problem turned out to be:
The marketing pages sold RAG as magic. In reality it behaves more like a database: if you don’t design for drift, invalidation, and observability, it will betray you at the worst moment.
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A real story of removing console-only changes, adding drift detection, and getting Terraform back in charge.
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We've shipped all three patterns to production. They're not interchangeable. Here's the framework we now use to decide which approach fits a given task.
We invalidate ~6% of LLM outputs before they reach a downstream system. Here's how we structure prompts and validators to catch malformed responses early.
We ran the same RAG workload across three vector stores for a quarter each. Here's what we learned about latency, cost, and operational overhead.
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