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A practical walkthrough on turning raw Markdown notes into a self-maintaining knowledge base, where AI agents handle tagging, sourcing, wiki-building, and visualization automatically.
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A developer just won VibeJam 2026 with a capybara game he built entirely through Claude Code, and the prize was $25,000. I read through his full...
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I was routing Claude Code through a proxy to mix in GPT models when I started digging into why my system prompts looked slightly off. Turns out I was...
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I have lost count of how many times Claude handed me a confident, well-formatted answer that turned out to be completely unverifiable. No sources, no hedging, just vibes dressed up as research.
I was building a research workflow where I needed Claude to pull technical information and surface sources I could actually check. What I kept getting instead was prose that sounded authoritative but had no traceable origin. The model wasn't lying, exactly -- it was pattern-matching toward what a correct answer looks like, which is worse in some ways because it's harder to catch.
A developer named Assaf Kip stumbled onto Anthropic's own documentation page specifically about reducing hallucinations. Not a blog post, not a third-party prompt guide -- Anthropic's internal docs on the problem. He found three system prompt instructions buried in there that most people building on Claude have apparently never seen. The instructions push the model to explicitly flag uncertainty, refuse to fabricate citations, and distinguish between what it knows and what it's inferring.
The difference in output quality he describes matches what I've seen when I tested similar constraints myself. When you give the model explicit permission to say "I don't know" or "I can't verify this," it actually uses that permission. Without it, the default behavior is to fill gaps with plausible-sounding content because that's what the training signal rewarded.
Kip didn't just post a screenshot and call it a day. He packaged the three instructions into a CLI tool and put the repo on GitHub so you can install it as a command and apply the system prompt without copying anything manually. That's the kind of thing that separates a useful find from a Reddit post you forget about in an hour.
The core idea is that Claude's hallucination problem is partly a defaults problem. The model defaults to completion, to helpfulness, to sounding confident. If your system prompt doesn't explicitly override those defaults for a research context, you get research-flavored text generation. Adding explicit instructions that reward uncertainty disclosure changes what the model optimizes for in that conversation.
This matters most for workflows where you actually act on the output. Code suggestions from a hallucinating model are annoying but usually obvious when you run the code. Factual research from a hallucinating model is dangerous because the error might not surface until much later, if ever.
I connected Kip's system prompt to my own Claude-based research setup and ran it against a few queries I had previously tested without it. The change is noticeable. The model hedges more, cites less when it can't verify, and flags gaps rather than papering over them. Some people will find that annoying because the outputs feel less polished. I find it useful because I can tell what I need to go verify myself.
The repo is at github.com/assafkip/research-mode. It's a small thing, but small things that fix a persistent annoyance are exactly what a workflow tool should be. If you're using Claude for anything where accuracy matters more than fluency, the five minutes to install this are worth it.
One honest caveat: this does not eliminate hallucinations. Claude will still occasionally confuse things or fill in details it shouldn't. What it does is reduce the silent confident-wrong outputs, which are the most expensive kind to deal with.
Tell us about the workflow you want to improve. We will help you identify the practical next step.