#context-engineering

#context-engineering

Knowing how to query ChatGPT, Gemini or Claude doesn't make you an expert on genAI and AI tools. There's a lot more to learn about in this fast-evolving area of tech. Even as thousands of workers lose their jobs to AI, the number of job openings seeking "AI skills" continues to grow. Mentions of AI skills in job postings rose 5% year over year, according to data released in December by staffing firm Manpower Group.

Context engineering has emerged as one of the most critical skills in working with large language models (LLMs). While much attention has been paid to prompt engineering, the art and science of managing context-i.e., the information the model has access to when generating responses-often determines the difference between mediocre and exceptional AI applications. After years of building with LLMs, we've learned that context isn't just about stuffing as much information as possible into a prompt.

For developers considering the leap from Claude Code to Codex CLI, this shift represents more than just a change in tools, it's a rethinking of how you approach workflows, task execution, and even problem-solving. With its impressive 272K token context window and open source adaptability, Codex CLI offers a tantalizing glimpse into what's possible. But before you make the switch, it's essential to understand the trade-offs and challenges that come with this upgrade.

Our understanding of the human body, particularly the brain and nervous system, has been profoundly shaped by the tools and technologies of each era.