RAG SystemsApr 22, 2026
A team had been swapping embedding models for two months trying to push retrieval recall past 60%. Each new model gave a couple of points then plateaued. The bottleneck was not the model. It was the architecture: a single embedding per chunk cannot match what token-level interaction can. Here is when ColBERT and ColPali earn their keep.
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RAG SystemsApr 15, 2026
A team asked us why their support assistant could not answer 'which enterprise customers complained about pricing in Q4 and what was their MRR'. The answer involved three entities, two relationships, and one timeframe filter. Vector search returns chunks, not answers to questions like that. Here is how Sapota decides when to add a graph layer.
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RAG SystemsApr 08, 2026
A founder forwarded the latest invoice from his vector DB provider with one comment: 'this needs to come down 80% by next quarter'. The corpus had grown three times in six months and the bill had grown with it. The fix was a one-line config change with a recall trade-off small enough that the team could not measure it on their eval set.
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RAG SystemsApr 01, 2026
A founder asked why their AI assistant kept saying 'the chart shows a positive trend' instead of reading the actual numbers. The pipeline was doing exactly what it was designed to do, and that was the problem. Here is how Sapota decides between summary-based and native multimodal RAG.
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RAG SystemsMar 25, 2026
A perfect 50-question demo on Tuesday. By the second week of production, users were filing tickets faster than the team could close them. The model had not changed. The retrieval pipeline had not changed. Here is what Sapota looks for first when a RAG launch goes sideways.
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RAG SystemsMar 18, 2026
A founder forwarded an email from his CFO three days before a board meeting: 'before we approve the AI budget, can engineering give us a number for accuracy?' The team had no number. They had vibes. Here are the three Ragas metrics Sapota ships before any RAG system goes to production.
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RAG SystemsMar 11, 2026
A team came to us frustrated that their semantic search was missing exact product codes like 'GPT-4o' even though those tokens appeared in the indexed content. The fix was a single config change. The reason most teams skip it is the same reason most teams ship vector-only search in the first place.
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