Frame your interaction with AI as if you're onboarding a new employee. Providing deep context, clear expectations, and even a mental "salary" forces you to take the task seriously, leading to vastly superior outputs compared to casual prompting.

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.

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.

To get the best results from AI, treat it like a virtual assistant you can have a dialogue with. Instead of focusing on the perfect single prompt, provide rich context about your goals and then engage in a back-and-forth conversation. This collaborative approach yields more nuanced and useful outputs.

The early focus on crafting the perfect prompt is obsolete.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.

Getting a useful result from AI is a dialogue, not a single command. An initial prompt often yields an unusable output. Success requires analyzing the failure and providing a more specific, refined prompt, much like giving an employee clearer instructions to get the desired outcome.

Moving beyond simple commands (prompt engineering) to designing the full instructional input is crucial. This "context engineering" combines system prompts, user history (memory), and external data (RAG) to create deeply personalized and stateful AI experiences.

Simply using one-sentence AI queries is insufficient. The marketers who will excel are those who master 'prompt engineering'—the ability to provide AI tools with detailed context, examples, and specific instructions to generate high-quality, nuanced output.