Author: Calder Rivenhall

Supply Chain Optimization with Generative AI: Demand Forecast Narratives and Exceptions

Discover how generative AI transforms supply chain demand forecasting by turning exceptions into actionable narratives. Learn about implementation challenges, accuracy gains, and future trends.

Few-Shot Prompting Strategies: Boost LLM Accuracy and Consistency

Discover how few-shot prompting boosts LLM accuracy by 15-40%. Learn expert strategies for example selection, ordering, and avoiding over-prompting traps.

LLM Tutoring: Building Personalized Learning Paths with AI

Discover how Large Language Models transform education by creating personalized learning paths. Learn about accuracy limits, implementation strategies, and ethical considerations for AI tutoring.

Prompt Robustness: Handling Noisy Inputs in LLMs

Learn why LLMs fail with messy user input and how to fix it. Discover proven strategies like Mixture of Formats and Robustness of Prompting to build reliable AI systems.

Managing Technical Debt in Vibe-Coded Projects

Vibe coding speeds up MVPs but hides costly technical debt. Learn how to manage comprehension gaps, enforce guardrails, and refactor AI-generated code before maintenance becomes a nightmare.

Role-Based Prompting: How Expert Personas Improve AI Responses

Learn how role-based prompting uses expert personas to enhance AI responses. Discover when it improves style versus accuracy, and master specific techniques for better outputs.

Calibrating Confidence in LLMs: Techniques and Metrics

Learn how to fix overconfident LLMs. Discover techniques like verbalized confidence, Thermometer scaling, and key metrics like ECE to ensure AI reliability.

From Rule-Based NLP to Large Language Models: A 30-Year Evolution

Trace the 30-year evolution of Natural Language Processing from rigid rule-based systems to modern Large Language Models. Discover how statistical methods, neural networks, and Transformers revolutionized AI's ability to understand and generate human language.