People struggle with AI prompts because the model lacks background on their goals and progress. The solution is 'Context Engineering': creating an environment where the AI continuously accumulates user-specific information, materials, and intent, reducing the need for constant prompt tweaking.

Instead of manually crafting a system prompt, feed an LLM multiple "golden conversation" examples. Then, ask the LLM to analyze these examples and generate a system prompt that would produce similar conversational flows. This reverses the typical prompt engineering process, letting the ideal output define the instructions.

With models like Gemini 3, the key skill is shifting from crafting hyper-specific, constrained prompts to making ambitious, multi-faceted requests. Users trained on older models tend to pare down their asks, but the latest AIs are 'pent up with creative capability' and yield better results from bigger challenges.

Pulse isn't just a feature; it's a strategic move. By proactively delivering personalized updates from chats and connected apps, OpenAI is building a deep user knowledge graph. This transforms ChatGPT from a reactive tool into a proactive assistant, laying the groundwork for autonomous agents and targeted ads.

Before delegating a complex task, use a simple prompt to have a context-aware system generate a more detailed and effective prompt. This "prompt-for-a-prompt" workflow adds necessary detail and structure, significantly improving the agent's success rate and saving rework.

The effectiveness of agentic AI in complex domains like IT Ops hinges on "context engineering." This involves strategically selecting the right data (logs, metrics) to feed the LLM, preventing garbage-in-garbage-out, reducing costs, and avoiding hallucinations for precise, reliable answers.

The early focus on crafting the perfect prompt is obsolete. Sophisticated AI interaction is now about 'context engineering': architecting the entire environment by providing models with the right tools, data, and retrieval mechanisms to guide their reasoning process effectively.

AI development has evolved to where models can be directed using human-like language. Instead of complex prompt engineering or fine-tuning, developers can provide instructions, documentation, and context in plain English to guide the AI's behavior, democratizing access to sophisticated outcomes.

The most leveraged engineering activity is creating a 'meta-prompt' that takes a simple feature request and automatically generates a detailed technical specification. This spec then serves as a high-quality prompt for an AI coding agent, making all future development faster.