Author: Tamara Weed - Page 4

Tamara Weed, Nov, 16 2025

Learn how supervised and preference-based fine-tuning methods impact AI hallucinations, and why faithfulness in reasoning matters more than output accuracy. Real data from 2024 studies show what works-and what doesn't.

Tamara Weed, Nov, 13 2025

Anti-pattern prompts in vibe coding lead to insecure AI-generated code. Learn the most dangerous types of prompts, why they fail, and how to write secure, specific instructions that prevent vulnerabilities before they happen.

Tamara Weed, Nov, 8 2025

Learn how to choose between IDEs, no-code, and low-code tools based on your skill level. See which platforms work best for beginners, intermediates, and professionals in 2025.

Tamara Weed, Nov, 6 2025

Enterprise LLMs demand more than uptime-they need clear SLAs on latency, compliance, data handling, and support. In 2025, providers like Azure OpenAI, Amazon Bedrock, and Anthropic compete on transparency, not just performance.

Tamara Weed, Nov, 4 2025

Vibe coding lets anyone build machine learning proof-of-concept apps using natural language prompts. No coding experience needed. Learn how it works, which tools to use, and the real risks you can't ignore.

Tamara Weed, Nov, 3 2025

Learn how access controls and audit trails protect sensitive data in LLM systems. Discover what logs to capture, how roles work, and why compliance isn't optional in 2025.

Tamara Weed, Oct, 22 2025

Ethical review boards for generative AI ensure responsible development by evaluating projects against fairness, privacy, and transparency standards. Learn how they work, who's on them, and what outcomes they deliver.

Tamara Weed, Oct, 18 2025

Learn how to protect personal data in LLM training pipelines using PII redaction and governance. Discover the best tools, techniques, and compliance strategies to avoid fines and data leaks.

Tamara Weed, Oct, 8 2025

Multi-agent systems with LLMs use teams of specialized AI agents to solve complex tasks more accurately than single models. Learn how frameworks like Chain-of-Agents, MacNet, and LatentMAS work, where they're used, and the risks involved.

Tamara Weed, Sep, 30 2025

Large language models learn by predicting the next word across trillions of internet text samples using self-supervised training. This method, used by GPT-4, Llama 3, and Claude 3, enables unprecedented language understanding without human labeling - but comes with major costs and ethical challenges.

Tamara Weed, Sep, 17 2025

Vibe coding lets knowledge workers build custom apps using plain English instead of code, saving 12-15 hours weekly. Tools like Knack, Memberstack, and Quixy make it possible - no programming skills needed.

Tamara Weed, Aug, 10 2025

Vibe coding uses AI to turn natural language into code, slashing development time and costs. Learn how to model ROI, avoid hidden debts, and decide if it's right for your project.