prompt-engineer

To structure a system prompt, you must architect the prompt by defining its role, context, instructions, constraints, and output format. This establishes the AI assistant's persona, capabilities, and operational boundaries for LLM-powered applications.

Few-shot example design involves creating illustrative examples within the prompt to guide LLM behavior. By providing specific input-output pairs, it demonstrates the expected response pattern and significantly improves the model's accuracy for targeted tasks.

Implement chain-of-thought prompting by instructing the LLM to break down tasks into sequential reasoning steps. This technique enhances complex problem-solving by forcing the model to process intermediate logical deductions before generating the final output.

The best way to manage the context window is through expert prompt engineering that optimizes context allocation. This involves structuring comprehensive system prompts and managing context constraints to ensure the LLM processes essential information without exceeding token limits.

Evaluate prompt outputs by applying structured prompt testing and output specification techniques. This process verifies that the LLM-generated responses strictly adhere to the requested formatting rules and consistently meet the desired accuracy criteria across various inputs.