Glossary

RLHF

Reinforcement Learning from Human Feedback (RLHF) is the training technique that transformed large language models from impressive autocomplete engines into useful assistants by systematically aligning their outputs with human preferences. First popularized by OpenAI's InstructGPT paper in 2022, the process trains a reward model on thousands of human comparisons—which response is better?—then uses reinforcement learning to tune the base model toward responses humans actually prefer. This alignment layer is why modern AI can follow nuanced instructions, refuse harmful requests, and match organizational tone—making it the invisible substrate beneath every enterprise AI deployment.

Referenced in these posts:

Satisficing for LLMs

By applying Herbert Simon’s concept of satisficing to AI, this post argues that language models might prefer logical‐sounding content over emotional appeals, mirroring human biases but inverted. It unveils a paradox: humans use emotion to decide rationally, while LLMs use pseudo‐rational style to appear helpful.

Related terms:

Strategic Software

Strategic software combines frontier AI models, custom code, and unique organizational expertise to tackle qualitative, strategic marketing challenges—from analyzing brand positioning and predicting market trends to optimizing customer journey orchestration. Its continuous learning loops compound organizational intelligence over time, delivering consultant-level insights at operational speed and scale.

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.

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.