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 complex 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:
Prompt Engineering
Prompt engineering involves designing and refining inputs—ranging from simple instructions to detailed system prompts with examples, constraints, personas, and chain-of-thought scaffolding—to elicit desired outputs from a language model. It’s the most accessible way to boost AI performance, requiring no training data or ML expertise, but prompts can be fragile, hard to version-control, and easy to overfit.
Structured Output
Structured output occurs when a language model returns data in predictable, machine-readable formats—such as JSON, XML, or typed objects—rather than free-form prose, enabling software systems to reliably parse fields like names, dates, and dollar amounts. By using constrained generation to enforce a JSON schema, structured output transforms AI from a conversational interface into a dependable system component.
Forward-Deployed Engineering
Forward-deployed engineering embeds engineers directly with clients to build custom solutions for real-world problems rather than shipping generic products from afar. Popularized by Palantir but long practiced in defense contracting and consulting, it closes the gap between AI demos and a company’s specific workflow by combining technical expertise with business context, data, and user needs.