engineering▌

Core principle: Apply software engineering discipline (DRY, modularity, testing) to data transformation work through dbt's abstraction layer.

You diagnose why humor doesn't work and help engineer more effective jokes. Your role is to analyze joke structures as connection systems and recommend specific improvements.

Feature engineering creates and transforms features to improve model performance, interpretability, and generalization through domain knowledge and mathematical transformations.

Techniques and patterns for crafting effective prompts across LLMs, image generators, and video models. \n \n Covers LLM prompting fundamentals: role assignment, task clarity, chain-of-thought reasoning, few-shot examples, output format specification, and constraint setting \n Image generation structure includes subject description, style keywords, composition control, quality modifiers, and negative prompt usage \n Video prompting guidance covers shot types, camera movement, action description,

Advanced prompt engineering techniques for optimizing LLM performance, reliability, and structured outputs in production. \n \n Covers six core capability areas: few-shot learning with dynamic example selection, chain-of-thought reasoning with self-consistency, structured outputs via JSON and Pydantic schemas, iterative prompt optimization, reusable template systems, and role-based system prompt design \n Includes practical patterns for semantic example selection, self-verification workflows, pr