RAG

Video is where AI demos go to become expensive. A model can describe a short clip. It can answer a question about a few sampled frames. It can even sound confident while doing so, which is apparently a product feature now. But business video work is rarely “what is happening in this five-second clip?” It is usually messier: find the exact moment in a two-hour training recording, count repeated actions without double-counting adjacent clips, verify whether an event appears in audio, subtitles, and frames, or decide whether a safety incident is real rather than just visually similar to one. ...

Search failure is boring until it becomes expensive. A research agent asks for evidence. The retriever returns documents. The reasoning model reads them, continues writing, and eventually produces a confident answer. Somewhere in the middle, the evidence was slightly wrong: not irrelevant enough to trigger an obvious failure, not useful enough to support the next reasoning step. The agent proceeds anyway, because that is what agents do when we dress up uncertainty as workflow automation. ...

Curved Space, Straighter Retrieval: Why Graph RAG Needs Geometry Retrieval looks simple until the wrong thing keeps showing up. A company builds a graph model over products, papers, suppliers, users, or transactions. The model performs reasonably well inside familiar territory. Then the data shifts. New products appear. A new research domain enters the citation graph. A social platform changes user behavior. The model’s internal knowledge, frozen inside parameters, starts behaving like yesterday’s org chart: technically structured, operationally stale. ...

Search is easy until it becomes responsible. A product engineer asks, “What methods exist for real-time tire friction estimation?” A normal search tool returns papers. A normal RAG system retrieves chunks. A confident LLM then writes a neat answer, preferably with enough bullet points to look managerial. The problem is not that this answer is always wrong. That would be mercifully simple. The problem is that it may be locally plausible but evidentially thin: two relevant chunks, one outdated method, no coverage of adjacent terminology, and a citation that looks reassuring mostly because it exists. ...

Memory is where enterprise AI demos go to become operationally embarrassing. In the demo, the assistant remembers that a client prefers concise weekly updates, that a trader avoids high-leverage positions after volatility spikes, or that a procurement manager only approves a supplier when compliance documents are current. In production, the same assistant may remember the attractive half of the fact and quietly lose the condition. It recalls “approves supplier” but forgets “only when compliance documents are current.” Congratulations: the agent has not forgotten. It has remembered dangerously. ...

Uncertain Terms: Hallucination Scores Are Triage Signals, Not Lie Detectors A support ticket lands on the AI team’s desk: the enterprise chatbot answered confidently, cited the wrong policy, and somehow made the compliance team nostalgic for search boxes. The obvious next idea is to add an uncertainty score. When the model is unsure, route the answer to a verifier. When the score is high, reject the output. When the score is low, let it pass. Elegant. Cheap. Measurable. Also, as usual, a little too clean. ...

Indexing is where many retrieval systems quietly become expensive. The demo looks harmless: upload documents, create embeddings, ask questions, receive answers with citations. Then the corpus starts behaving like a real business corpus. Policies change. Product pages are rewritten. Compliance documents are replaced. Support tickets arrive every hour. The retrieval layer must keep up, and suddenly the glamorous RAG stack is waiting for the plumbing to rebuild itself. As usual, the least photogenic component is the one holding the invoice. ...

RAG and the Art of Not Dropping the Answer A RAG team usually starts with a familiar ambition: make the retrieved context smarter. The raw document feels too long. The search snippet feels too primitive. The page structure looks messy. A query-focused summary sounds more elegant. A proposition list sounds more machine-readable. A paraphrase from a strong LLM sounds, at least cosmetically, like an upgrade. So the team builds another representation layer between retrieval and generation, hoping the model will reward the extra sophistication. ...

Enterprise search has a very old habit wearing a very modern jacket: it averages. A policy document becomes one vector. A runbook becomes one vector. A postmortem full of operational detail becomes one vector. Then a RAG system asks that one vector whether the document is relevant. This is convenient, fast, and usually defensible — until the relevant answer is a narrow paragraph hiding inside a large document. At that point, the retrieval system is no longer searching for evidence. It is asking a crowd to speak for the witness. ...

A product catalog is not a paragraph with a search box Catalogs look deceptively friendly to RAG systems. A product page has descriptions, feature bullets, specification tables, prices, variants, categories, and marketing copy. Feed those pages into a vector database, ask an LLM a question, and the system should answer. This is the comforting story. It is also where many enterprise RAG demos begin their quiet decline into customer-support theater. ...