Engineering Better Outputs
An LLM is only as good as its instructions. We don't just write prompts; we engineer interaction frameworks. By applying cognitive science principles to model inputs, we unlock reasoning capabilities that standard prompting misses.
Techniques We Use
- Chain-of-Thought (CoT): Guiding the model to "show its work" step-by-step, significantly improving accuracy on complex logic and math tasks.
- ReAct (Reason + Act): A framework where the model reasons about a task, performs an action (like a search), and then reasons again based on the result.
- Few-Shot Prompting: Providing curated examples within the prompt context to steer the model's style, tone, and format without fine-tuning.
