Few-Shot Prompting
Few-shot prompting leverages AI's pattern recognition capabilities by providing examples within the prompt itself. This technique transforms a simple query into a learning opportunity—the AI identifies patterns in your examples and applies them to generate responses that match your intended style, format, or approach.
Unlike traditional training that requires massive datasets, few-shot prompting enables real-time adaptation through just a handful of examples. It's particularly powerful for establishing consistent voice, formatting specifications, or domain-specific outputs without any model fine-tuning.
Some best practices:
- Select high-quality, diverse examples that represent your desired output
- Avoid unintentional pattern creation—mix examples strategically to prevent over-narrowing
- Maintain a repository of proven examples for consistent results across teams
This approach democratizes AI customization, allowing any user to guide model behavior through thoughtful example selection rather than technical expertise.
Related terms:
Accretive Software
Accretive software refers to AI platforms that automatically absorb model improvements as margin expansion by treating models as interchangeable components and routing queries to the optimal model in real time. Rather than fighting obsolescence, these platforms convert every efficiency breakthrough into customer value or profit margin.
WWGPTD
WWGPTD began as internal Slack shorthand to remind teams that using AI isn’t cheating but the essential first step. The accompanying bracelets serve to normalize AI as a fundamental tool for creating better work.
Fuzzy Interface
A fuzzy interface is AI’s adaptive translation layer between rigid organizational systems and human intent, interpreting context and adapting to various inputs without perfect data standardization. This capability bridges legacy systems and modern tools—translating formats, enabling natural language interaction, and handling technical integration and compliance behind the scenes.