prompting

Writing an effective system prompt requires structuring directions clearly using markdown, imposing strict constraints, and iterating on prompt design to optimize token efficiency and agent performance.

The best way to format system prompts is using markdown structures and providing examples, which helps organize directions clearly and ensures the LLM understands constraints and expected behaviors.

Using the same system prompt across different LLM architectures is not optimal; prompts should be tailored and tuned specifically for models like Claude, GPT, Gemini, and Codex to achieve maximum effectiveness.

Inconsistent LLM agent outputs often result from poorly designed system prompts lacking clear constraints, insufficient formatting, or a lack of model-specific tuning and iteration strategies.

To impose constraints on an AI agent, you must design the system prompt with explicit rules, clear boundaries, and structured formatting that guide the LLM's behavior and limit unwanted actions.

You need to optimize token efficiency in prompt design when building AI applications or agent configurations, as concise prompts reduce latency and costs while maintaining clear directions and constraints.