System Prompt Design

System prompts are the standing instructions that shape how a large language model behaves before a user types anything. They set the model's role, tone, boundaries, output format, and approach to reasoning. As LLMs move from chat demos into production software, the system prompt has become engineering infrastructure that teams version, test, and debug like any other code.

Why it matters now

The discipline is changing quickly. Early prompt work focused on clever wording and on coaxing models to reason step by step. Much of the attention has since shifted toward structure. Developers now define schemas that outputs must follow. They also rely on inference-time techniques such as constrained decoding to guarantee valid formats, rather than cleaning up responses afterward with pattern matching. Meanwhile, researchers keep refining reasoning strategies, such as breaking problems into nested steps or combining multiple reasoning paths to improve reliability.

The stakes go beyond developer convenience. Public bodies and independent evaluators are testing how different models, including open-weight releases from around the world, respond to instructions. As a result, how prompts are written and evaluated is becoming part of broader conversations about safety, transparency, and trust.

What you'll find here

This hub tracks developments across the field, including:

• Prompting techniques: new reasoning frameworks and evidence on when they help or hurt
• Structured outputs: schemas, function calling, and decoding methods that enforce format
• Evaluation and testing: how models are assessed on instruction-following and robustness
• Best practices: evolving guidance for writing, maintaining, and securing system prompts

These stories explain how the craft is evolving, whether you build with LLMs or simply want to understand how they're steered.