You can't just slap retrieval onto an LLM and call it production-ready. No wonder most RAG projects fail!
Most AI teams spend weeks perfecting their embeddings, only to realize they have no idea if their retriever is actually finding relevant docs. Or worse, their system confidently cites completely wrong information because nobody measured groundedness.
The wake-up call always comes the same way: "Why is our chatbot making stuff up?"
Context relevance ≠ answer quality.
Retrieval precision ≠ user satisfaction.
Faulty evaluation pipelines shouldn't derail your progress.
Future AGI just dropped a guide that covers what I wish every team knew before they shipped. Real metrics that matter, not vanity numbers.
Worth a read 👇
https://futureagi.com/blogs/rag-evaluation-metrics-2025
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