Author: Bekah Funning - Page 2
Auditing AI Usage: Logs, Prompts, and Output Tracking Requirements
AI auditing requires detailed logs of prompts, outputs, and context to ensure compliance, reduce legal risk, and maintain trust. Learn what to track, which tools work, and how to start without overwhelming your team.
Emergent Abilities in NLP: When LLMs Start Reasoning Without Explicit Training
Large language models suddenly gain reasoning skills at certain sizes-without being trained for them. This phenomenon, called emergent ability, is reshaping AI development-and creating serious risks.
Evaluating Reasoning Models: Think Tokens, Steps, and Accuracy Tradeoffs
Reasoning models improve accuracy on complex tasks but at a steep cost in tokens and dollars. Learn when they help, when they hurt, and how to use them wisely without breaking the bank.
Value Capture from Agentic Generative AI: End-to-End Workflow Automation
Agentic generative AI is transforming enterprise workflows by automating end-to-end processes without human intervention. Discover how companies are capturing 20-60% productivity gains and real ROI in 2025.
How to Calibrate AI Personas for Consistent Responses Across Sessions and Channels
Learn how to keep AI personas consistent across sessions and channels using structured prompts, memory systems, and human oversight. Fix persona drift before users lose trust.
Red Teaming for Privacy: How to Test Large Language Models for Data Leakage
Learn how to test large language models for data leakage using red teaming techniques. Discover real-world risks, free tools like garak, legal requirements, and how companies are preventing privacy breaches.
How to Prompt for Performance Profiling and Optimization Plans
Learn how to use performance profiling tools effectively by asking the right questions to find true bottlenecks. Avoid guesswork and optimize with data-driven insights for real performance gains.
Data Collection and Cleaning for Large Language Model Pretraining at Web Scale
Training large language models requires more than raw data-it demands meticulous cleaning. Discover how web-scale datasets are filtered, deduplicated, and refined to boost model performance-and why quality beats quantity.
Causal Masking in Decoder-Only LLMs: How It Prevents Information Leakage and Powers Generative AI
Causal masking is the key architectural feature that enables decoder-only LLMs like GPT-4 and Llama 3 to generate coherent text by blocking future token information. Learn how it works, why it's essential, and how new research is enhancing it without breaking its core rule.
Vision-Language Applications with Multimodal Large Language Models: What’s Working in 2025
Vision-language models are now transforming document processing, healthcare, and robotics by combining image and text understanding. In 2025, open-source models like GLM-4.6V are outperforming proprietary systems in key areas - but only if deployed correctly.
Education Projects with Vibe Coding: Teaching Software Architecture Through AI-Powered Examples
Vibe coding is transforming how software architecture is taught by letting students build real apps with AI, focusing on design over syntax. Early results show faster learning, deeper understanding, and broader access to programming education.
v0, Firebase Studio, and AI Studio: How Cloud Platforms Support Vibe Coding
Firebase Studio, v0, and AI Studio are transforming how developers build apps using natural language and AI. Learn how vibe coding works, which tool to use for what, and why this is the future of development.
