Blog
Ideas and guides from pin
The failure that should worry you about RAG isn't a missing answer. It's a fluent answer built from the wrong five paragraphs.
How retrieval-augmented generation actually works — chunking, embeddings, hybrid retrieval, re-ranking, context assembly — and the five distinct mechanisms by which it produces a plausible wrong answer instead of an honest 'not found.' Grounded in the published literature, not in a vendor diagram.
When a model explains its reasoning, it isn't reporting what happened inside. It's generating more text.
How inference mechanically works in a modern LLM — the forward pass, the KV cache, sampling, quantization and what each one actually costs — and what interpretability research can and cannot currently tell you about why a model produced an output. Part 2 of our technical-depth series.
The failure that should worry you about OCR doesn't look like garbled text. It looks like 98% confidence and a missing paragraph.
We built our OCR pipeline as a confidence-routed cascade, benchmarked it against three newer document-understanding tools, then threw genuinely dirty scans at it and watched the safety net fail to catch four real bugs. What we found, and what the current literature actually supports.