Much less hassle if it knows how to solve this problem out of the box – I've run servers for it and logged them to files and told it to read the logs but it's a lot of extra friction
PROMPT ENGINEERING
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AI Agent Breakdown: Reasoning, Planning, Tools, and Autonomy
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What is an AI Agent? Here’s your no-BS breakdown: – It reasons, plans, acts, learns, adapts, delegates
– Uses tools (web, APIs, data, memory)
– Works with goals, context, and system prompts
– Can be multi-agent, autonomous, or orchestrated
– Agent ≠ Agentic AI (don’t get -
GPT-5 Can Be Guided With Different Prompting Approaches
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Tu as tout résumé, je peux dire qu’avec GPT-5 j'arrive à le mener à faire ce que je veux, c'est juste qu'il faut le prompter différemment. Et ça les gens, n'aiment pas
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Prompt Injection Security: Design Patterns and Prevention
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Not that let you use all three legs of the lethal trifecta at the same time For prompt injection generally I like these two papers: https://
simonwillison.net/2025/Apr/11/ca
mel/
… and https://
simonwillison.net/2025/Jun/13/pr
ompt-injection-design-patterns/
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ChatGPT Study Mode: AI Tutoring Features and Educational Impact
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🎓 ChatGPT Study Mode… hype or not?
— Louis-François Bouchard 🎥🤖 (@Whats_AI) 9 août 2025
TL;DR: it's a prompt transforming ChatGPT into a tutor-style chatbot: questions, hints, self-reflection, scaffolded steps instead of a direct answer.
• Personalization + quizzes/feedback to check understanding
• Since it's (for now)_ just… pic.twitter.com/9CJ6USmpR3ChatGPT Study Mode… hype or not? TL;DR: it's a prompt transforming ChatGPT into a tutor-style chatbot: questions, hints, self-reflection, scaffolded steps instead of a direct answer. • Personalization + quizzes/feedback to check understanding
• Since it's (for now)_ just -
Agent Reliability: Catching Hallucinations and Verifying Groundedness
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Agent Reliability A practical guide to help you catch hallucainations, verify groundedness, and monitor tool usage for LangChain/LangGraph applications: https://
docs.futureagi.com/cookbook/cookb
ook13/Adding-Reliability-to-Your-LangChain-LangGraph-Application-with-FutureAGI
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LLMs Cannot Distinguish Context: Collaboration vs High-Stakes Evaluation
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Two situations: 1 I wave a co-worker to my monitor to show them a file I have open. "Is this right"? 2 I sit down someone at a table. They have 2 hours to respond. This is an exam. The stakes are high. "Is this right?" The humans collaborator knows 1 vs 2. The LLM doesn't know
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Using AI Tools to Package Projects for Model Conversations
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I'm starting to do some of this too. I have a script that packages all of the files of my project (which isn't a giant repo and fits just fine) uses `files-to-prompt`, then I start new conversation, copy paste, and ask a question at the end, and manually select appropriate model.
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LLMs Becoming Too Agentic By Default
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I'm noticing that due to (I think?) a lot of benchmarkmaxxing on long horizon tasks, LLMs are becoming a little too agentic by default, a little beyond my average use case. For example in coding, the models now tend to reason for a fairly long time, they have an inclination to
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CaMeL Approach Security: Preventing Untrusted Content in AI Prompts
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You mean the CaMeL approach? Yeah actually implementing that well looks really difficult to me, you have to make about sure that any untrusted content is "tainted" and is then NEVER included in a prompt that might trigger tools