prompt-engineering

Chain-of-thought prompting in Gemini is implemented by structuring prompts that guide the model through sequential reasoning steps. This approach ensures accurate logical deduction and improved performance on complex reasoning tasks.

Handling large context windows requires optimizing prompt structure to leverage Gemini's capacity effectively. This involves refining token usage and organizing system instructions to process complex, data-heavy inputs without exceeding limits.

Few-shot learning improves LLM output accuracy by providing specific examples within the prompt to guide the model's responses. This technique ensures adherence to desired output formats and reasoning styles for complex tasks.

Yes, you can build self-correction loops into system prompts. This Skill helps design prompts that instruct the model to review, identify errors, and improve its own output before returning the final result.

Optimizing token usage for long documents involves moving fixed rules to the system prompt and leveraging large context windows efficiently. This approach reduces redundant tokens and ensures accurate data extraction from extensive inputs.