1. Replace Vague Demands with Strict Constraints
The single most common mistake in prompting is asking a model to 'write a good post' or 'make this sound professional'. Without boundary conditions, modern LLMs revert to the statistical average of all internet prose—resulting in buzzwords, corporate passive voice, and hollow cheerleading.
Instead, supply explicit negative constraints: 'Do not use words like navigate, delve, landscape, or revolutionize. Keep paragraphs to 2 lines max. Use active verbs only.'
Key Principles
- •State what NOT to do explicitly in a dedicated constraints block.
- •Specify the exact format (bullet list, table, markdown headers).
- •Set target character or word limits.
2. Provide a Crystal-Clear Role and Calibrated Audience
Language models adjust their vocabulary distribution based on the persona assigned. Don't simply say 'Act as an expert'. Be granular: 'Act as a Senior Staff Performance Engineer auditing a Node.js microservice experiencing p99 latency spikes during Black Friday.'
Key Principles
- •Specify both the expert role AND the target reader's seniority level.
- •Define the consequence of failure to heighten the model's analytical rigor.
3. The Power of Few-Shot Examples
Giving one or two high-quality examples of your desired input/output pairing increases output quality by over 80%. Show the model what a 10/10 answer looks like before requesting yours.
Key Principles
- •Include one input example and one desired output example.
- •Highlight the specific tone nuances you appreciate in the example.