Category: History

Evaluation Gates and Launch Readiness for Large Language Model Features

Evaluation gates are mandatory checkpoints that ensure LLM features are safe, accurate, and reliable before launch. Learn how top AI companies test models, the metrics that matter, and why skipping gates risks serious consequences.

When to Transition from Vibe-Coded MVPs to Production Engineering

Vibe coding gets you to market fast, but it collapses under real user load. Learn the exact triggers-user count, speed drop, security flaws-that mean it’s time to stop coding by feel and start building for scale.

Accessibility-Inclusive Vibe Coding: Patterns That Meet WCAG by Default

Accessibility-Inclusive Vibe Coding integrates AI code generation with WCAG-compliant patterns to make accessibility automatic, not optional. Learn how tools like GitHub Copilot and axe MCP Server are transforming development in 2025.

Why Generative AI Hallucinates: The Hidden Flaws in Language Models

Generative AI hallucinates because it predicts text based on patterns, not truth. Learn why even the most advanced models like GPT-4 and Claude 3 invent facts, how this affects real-world use, and what you can do to stay safe.

Vibe Coding vs AI Pair Programming: When to Use Each Approach

Vibe coding speeds up routine tasks with AI-generated code, while AI pair programming offers real-time collaboration for complex problems. Learn when to use each to boost productivity and avoid security risks.

Understanding Per-Token Pricing for Large Language Model APIs

Per-token pricing is the standard way LLM APIs charge users-paying for every word read and written. Learn how tokens work, why output costs more, and how to avoid surprise bills on GPT-4, Claude, and other AI models.

How Cross-Functional Committees Ensure Ethical Use of Large Language Models

Cross-functional committees are essential for ethical Large Language Model use, combining legal, security, privacy, and product teams to prevent bias, leaks, and legal violations before they happen.

Token Probability Calibration in Large Language Models: How to Make AI Confidence More Reliable

Token probability calibration makes AI confidence scores match reality. Learn how GPT-4o, Llama-3, and other models are being fixed to stop overconfidence and improve reliability in healthcare, finance, and code generation.

Code Generation with Large Language Models: Boosting Developer Speed and Knowing When to Step In

AI code generators like GitHub Copilot and CodeLlama boost developer speed by up to 55% on routine tasks-but they also introduce security flaws and bugs. Learn where they help, where they fail, and how to use them safely in 2025.

Quality Control for Multimodal Generative AI Outputs: Human Review and Checklists

Human review and structured checklists are essential for catching hidden errors in multimodal AI outputs that automated systems miss. Learn how to implement proven frameworks in biopharma, manufacturing, and regulated industries.

Benchmarking Bias in Image Generators: How Diffusion Models Reinforce Gender and Race Stereotypes

Diffusion models like Stable Diffusion amplify racial and gender stereotypes in generated images, underrepresenting women in leadership and overrepresenting Black individuals in low-status roles. Real-world harm is already happening in hiring and education.

Document Intelligence Using Multimodal Generative AI: PDFs, Charts, and Tables

Multimodal generative AI now reads PDFs, charts, and tables together-understanding how text, images, and data connect. Learn how it outperforms old OCR systems and where it's being used today.