Hypothesis
Structured prompt templates with clear constraints produce more consistent and higher-quality AI output than ad-hoc prompting.
Approach
Built a library of prompt frameworks organized by use case:
- Analysis prompts — For breaking down complex problems
- Generation prompts — For creating content, code, or designs
- Evaluation prompts — For reviewing and critiquing work
- Synthesis prompts — For combining multiple inputs into coherent output
Key Findings
- Constraints improve output — Prompts with explicit format requirements, word limits, and structural guidelines produce better results than open-ended requests.
- Examples are worth 100 words of instruction — Including 1-2 examples in the prompt consistently outperforms verbose instructions.
- Chain of thought matters — Asking the AI to think step-by-step produces more reliable results for complex tasks.
- Temperature control is underrated — Lower temperatures for factual tasks, higher for creative tasks. Simple but effective.
Outcome
The framework is now part of my daily workflow. Prompt quality has a direct, measurable impact on output quality. Treating prompts as engineering artifacts — with version control, testing, and iteration — has been the single biggest improvement to my AI workflows.