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:
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.
Conway's Law
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.
RLHF
Reinforcement Learning from Human Feedback (RLHF) trains a reward model on human preference comparisons and uses reinforcement learning to align language model outputs with those preferences, transforming them from autocomplete engines into useful assistants. First popularized by OpenAI’s InstructGPT in 2022, RLHF enables AI to follow nuanced instructions, refuse harmful content, and match organizational tone.