Anthropic's guide to prompt engineering best practices for 2026 walks through a progression of techniques for getting better results from AI models like Claude. The article starts with core techniques — being explicit and clear, providing context and motivation, adding specificity, using examples (few-shot prompting), and giving the model permission to express uncertainty. These foundational habits are positioned as the starting point before moving to more sophisticated methods.

The guide then covers advanced techniques including response prefilling to enforce output formats like JSON, chain of thought prompting (with basic, guided, and structured variants), output format control, and prompt chaining — breaking complex tasks into sequential steps across multiple prompts. Notably, the article acknowledges that some once-popular techniques like XML tags and heavy role prompting are now less necessary with modern models like Claude 4.x, which can interpret structure and intent more naturally.

A practical decision framework helps readers choose the right technique for their situation, while a troubleshooting section addresses common problems like generic responses, hallucinations, and inconsistent formatting. The article emphasizes that prompt engineering is converging with context engineering for newer Claude models, where the focus shifts from scaffolding prompts to curating the right context. The overarching advice: start simple, add complexity only when needed, and remember that the best prompt is the one that achieves goals reliably with minimum structure.

Aaron Levie, CEO of Box, argues that context is king for AI agents, emphasizing that the ability to design systems providing agents with optimal context will become a critical competitive advantage. He highlights that knowledge work has always been relatively messy, implying that structuring and surfacing the right information for AI agents represents both a significant challenge and a major opportunity for individuals, teams, and companies alike.

The post underscores a growing theme in enterprise AI: that the value of agents is directly tied to the quality of context they receive. This positions context engineering — the practice of curating and delivering relevant information to AI systems — as a key discipline in the next phase of AI adoption.

Aaron Levie, CEO of Box, shares a core product management principle for AI agents: the key challenge is determining what a very smart person — starting with zero context — would need to successfully complete a given task. He argues that the entire "game" of AI agent product design comes down to getting exactly the right information into the context window. This insight highlights how building effective AI agents is less about raw intelligence and more about context engineering — curating and delivering the precise inputs an agent needs to reason and act correctly.