The Letaido Blog
For marketers who want to use AI to automate their work, grow their business, and stay at the absolute bleeding edge of what AI can do.
Context engineering is the practice of giving AI agents the right information to act well. Here's what it means, why it matters, and how it works in practice.
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Loop engineering is the practice of designing an AI agent's act-observe-iterate cycle. Here's what good loops look like — and what causes them to break.
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Harness engineering designs the tools, constraints, memory, and feedback loops an AI agent operates within. Here's what each layer does and how to build it.
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Agentic engineering is the discipline of building AI systems that pursue goals, not just answer questions. Here's what it means and why it matters.
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Microsoft is building a Copilot super app, OpenAI cut GPT-5.6 prices, and Nvidia may backstop up to $250B of OpenAI infrastructure.
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Opus 5, Gemini updates, and a $5B AMD-Anthropic deal headline the product and money side. OpenAI’s sandbox escape raises the policy temperature.
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Google’s flagship model is delayed. Moonshot and Nvidia pushed new model plans. Anthropic, Databricks, and Reflection kept the money flowing.
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Prompt engineering shapes the input. Context engineering controls everything the model sees. Here's what the difference means for AI that actually works at scale.
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A system prompt sets the rules every AI response follows. Learn what one is, how it works, and how to write one that actually controls AI output.
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Prompt chaining splits complex AI tasks into focused steps so each output feeds the next. Learn how it works, when to use it, and how to build a chain.
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