Durable security, privacy, and failure-boundary concepts for using LLMs and building LLM applications.
Tutorials
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Your RAG app is working beautifully. Users ask questions, your system retrieves the right documents, and the model produces clear, grounded answers. Then…
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You're staring at a chat window. You've drafted a prompt that would save you an hour of work—if you paste in the real document. The cursor blinks. Your…
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The most common mistake I see when teams first put an LLM into a real workflow is also the most understandable one: they bolt a human approval step onto…
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The moment you give an agent a tool, you stop deciding what it does and start deciding what it is allowed to do. The real design question is not "can the…
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If you've spent any time reading about AI security, you've probably seen "jailbreak" and "prompt injection" used interchangeably. The terms blur because…
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You have a real document you want an LLM to help with—maybe a customer support ticket, a contract, or a set of notes. It's full of useful context. It's…
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A support chatbot answers a customer's question with total confidence. The answer cites a document from the company knowledge base. The only problem: the…
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