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:
This approach democratizes AI customization, allowing any user to guide model behavior through thoughtful example selection rather than technical expertise.
Conway’s Law states that organizations designing systems are constrained to produce designs mirroring their own communication structures. For example, separate sales, marketing, and support teams often yield a website organized into Shop, Learn, and Support sections—reflecting internal divisions rather than user needs.
Gravity wells describe economic dynamics where scarce resources flow disproportionately to entities with the greatest ability to pay and deploy, creating self-reinforcing concentrations of power. In the AI economy, they form around critical bottlenecks in compute, power, and talent, determining who captures resources and who scrambles for scraps.
Zero-shot prompting is the most basic form of AI interaction where questions are posed without any examples or guidance, relying entirely on the model’s pre-trained knowledge. This baseline approach immediately tests raw capabilities, revealing both its breadth and limitations.