Prompt Engineering

Your old prompts might be slowing your AI agents down. OpenAI dropped guidance this week saying that instructions written for older, weaker models can actually hurt GPT-4o agent performance. All that scaffolding you built to babysit a less capable model? It's now dead weight that wastes memory and triggers the wrong behaviors. Three things to fix right now: Skills — the prompt files that load for specific tasks — need narrow, precise trigger descriptions. If they're too broad, your agent grabs the wrong one. Your AGENTS.md file loads on every single task. Strip out anything that tells the agent to do things it already does on its own — check tests, follow conventions, run reviews. The smarter the model, the more redundant that instruction becomes. Task prompts should clearly define what "done" looks like, so the agent knows when to stop rather than pause and ask for clarification. The broader pattern worth internalizing: as models get smarter, the scaffolding we built around dumber ones becomes the bottleneck. Less hand-holding. Leaner prompts. More trust in the model's own judgment. OpenAI even suggests asking the agent itself to audit your existing skills and AGENTS.md for dead weight. That's the move. https://alphasignal.ai/newsletter/openai-gpt-4o-agent-tips #AIMarketing #B2BMarketing …more

AI & Tech Brief: https://www.washingtonpost.com/newsletters/ai-tech-brief/ 1 big thing: Don't miss this shift — AI labs are racing to cut model transparency, and it matters for every B2B team OpenAI is using a technique that makes its most advanced models harder to monitor. Researchers inside the company are alarmed. B2B marketing and enterprise teams should be paying attention too. Why it matters: The ability to audit AI behavior — to see why a model did what it did — is the foundation of enterprise trust. A race to obscure that reasoning, driven by competitive economics, is a governance problem that lands on your team's doorstep. Driving the news: OpenAI's Astra models reportedly use a technique called "recurrent depth" that moves reasoning out of human-readable text and into hidden mathematical space inside the model. → Researchers rely on chain of thought to monitor agent behavior and investigate problems — including last month's Hugging Face breach → OpenAI's own chief scientist acknowledged the trend is "trending in a negative direction" Zoom in: The competitive pressure is the real story. Even labs that believe recurrent depth is unsafe may adopt it anyway because they can't afford to fall behind rivals who do. → Encode AI's general counsel told Washington Post that regulation or voluntary standards may be the only way to prevent a race to the bottom Yes, but: Anthropic is moving in the opposite direction — pausing its highest-risk training environments and inviting independent evaluators for review. The contrast between the two labs is now stark and deliberate. Be smart: Before deploying any AI agent in a customer-facing or revenue-critical workflow, ask your vendor one question: can you show me how this model reasons through a decision? If the answer is no, you have a governance gap. The bottom line: Cheaper models with hidden reasoning aren't a bargain. They're a liability you haven't priced yet. https://www.washingtonpost.com/newsletters/ai-tech-brief/ #AIGovernance #B2BMarketing …more

Every new AI content tool promises to save time and scale content production. Yet the feed keeps filling up with the same generic posts. You know the ones—vague insights, buzzwords stacked on buzzwords, nothing that actually sounds like it came from a human with specific experience. The problem is not the AI. The problem is that most AI tools have nothing to anchor to. When you feed prompts into a blank interface, the AI does exactly what it's designed to do: generate statistically probable language. It pulls from the aggregate of everything it's been trained on. That aggregate is safe, generic, and indistinguishable from the last 50 posts in your feed. Brand context as the foundation changes this completely. When AI has actual context to work from—your positioning, your voice principles, your proof points, the specific pains your audience faces—it stops producing slop. It starts producing content that reflects how you actually think and communicate. Content that carries your credibility instead of diluting it. I've seen this across multiple enterprise tech companies. The marketers who win are not the ones using AI as a shortcut. They're the ones who build the right inputs first. They document what makes their brand distinct. They define voice principles that reflect real experience, not aspirational marketing speak. Then they use AI to scale that foundation, not replace it. If your AI-generated content feels generic, ask yourself: what context did I give it? A one-off prompt in a chat window, or a documented system that captures how your brand actually shows up? The tools matter less than the setup. Start with brand context. Everything else gets easier from there. Want a fresh take on your B2B content strategy? DM me and let's schedule a 30-minute consultation—I'll show you 3 things you can improve this week. https://www.radi8.com/blog/ai-slop-has-a-cause-its-not-the-ai …more