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Here's a question worth sitting with: when did you last fine-tune, retrain, or change the prompt on your production model — and how do you know it didn't make things worse? Not "did it feel better in the five examples you tried." How do you know. If your answer is "the demo looked good" or "the team felt like it was more helpful," you're not alone — and you're also flying blind. The Pattern We See Constantly A team ships v1. It works well enough. A few months in, they fine-tu

You shipped the demo. It looked great. The retrieval worked. The model responded fluently. The investors nodded. The Slack channel celebrated. Then you deployed to production. Within 30 days, your support queue filled with complaints. The model was confidently wrong. It hallucinated facts that were nowhere in your documents. It ignored your output format half the time. It worked fine on the test queries and broke on real user inputs. Your inference bill was 3x the estimate. A

If you are building an LLM-powered product in 2026, writing code or integrating an API is the easy part. The hard part is everything that comes after: How do you know your model actually works on your domain? How do you prove it improved after fine-tuning? How do you stop it from hallucinating in production? How do you align its behavior to what your users expect? These are not product questions. They are LLM training research questions — and most engineering teams do not hav

You followed the tutorial. You copied the code. Your AI chatbot answers questions perfectly on your laptop. Then you try to ship it. The API times out under real load. The vector search returns garbage when the query doesn't match training examples exactly. There is no error handling, so one bad request crashes the whole service. You have no idea if it is even working correctly because there is no logging. The chunking strategy that worked on your sample PDF breaks on a scann

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Retrieval-Augmented Generation (RAG): The Paper That Grounded AI in Real Knowledge Published by Codersarts · AI Research Paper Series | https://labs.codersarts.com/ The Paper at a Glance Title Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks Authors Lewis, Perez, Piktus, Petroni, Karpukhin, Goyal, Küttler, Lewis, Yih, Rocktäschel, Riedel, Kiela Institution Facebook AI Research (FAIR) Published 2020 arXiv arxiv.org/abs/2005.11401 Citations 10,000+ What This Pap