Tri-City AI Links
Refusal-Proofing Security Requirements: Prompts That Demand Safe Defaults
Refusal-proof security requirements eliminate insecure defaults by making safety mandatory, measurable, and automated. Learn how to write prompts that force secure configurations and stop vulnerabilities before they start.
Governance Committees for Generative AI: Roles, RACI, and Cadence
Learn how to build a generative AI governance committee with clear roles, RACI structure, and meeting cadence. Real-world examples from IBM, JPMorgan, and The ODP Corporation show what works-and what doesn't.
Positional Encoding in Transformers: Sinusoidal vs Learned for Large Language Models
Sinusoidal and learned positional encodings were the original ways transformers handled word order. Today, they're outdated. RoPE and ALiBi dominate modern LLMs with far better long-context performance. Here's what you need to know.
Benchmarking Vibe Coding Tool Output Quality Across Frameworks
Vibe coding tools are transforming how code is written, but not all AI-generated code is reliable. This article breaks down the latest benchmarks, top-performing models like GPT-5.2, security risks, and what it really takes to use them effectively in 2025.
Model Distillation for Generative AI: Smaller Models with Big Capabilities
Model distillation lets you shrink large AI models into smaller, faster versions that keep 90%+ of their power. Learn how it works, where it shines, and why it’s becoming the standard for enterprise AI.
Security Hardening for LLM Serving: Image Scanning and Runtime Policies
Learn how to harden LLM deployments with image scanning and runtime policies to block prompt injection, data leaks, and multimodal threats. Real-world tools, latency trade-offs, and step-by-step setup.
Shadow AI Remediation: How to Bring Unapproved AI Tools into Compliance
Shadow AI is the unapproved use of generative AI tools by employees. Learn how to detect it, bring it into compliance, and avoid massive fines under GDPR, HIPAA, and the EU AI Act with practical steps and real-world examples.
Vision-First vs Text-First Pretraining: Which Path Leads to Better Multimodal LLMs?
Text-first and vision-first pretraining are two paths to building multimodal AI. Text-first dominates industry use for its speed and compatibility. Vision-first leads in complex visual tasks but is harder to deploy. The future belongs to hybrids that blend both.
Safety in Multimodal Generative AI: How Content Filters Block Harmful Images and Audio
Multimodal AI can generate images and audio from text-but it also risks producing harmful content. Learn how safety filters work, which providers lead in blocking dangerous outputs, and why hidden attacks in images are the biggest threat today.
Guardrails for Medical and Legal LLMs: How to Prevent Harmful AI Outputs in High-Stakes Fields
LLM guardrails in medical and legal fields prevent harmful AI outputs by blocking inaccurate advice, protecting patient data, and avoiding unauthorized legal guidance. Learn how systems like NeMo Guardrails work, their real-world limits, and why human oversight is still essential.
How Analytics Teams Are Using Generative AI for Natural Language BI and Insight Narratives
Analytics teams are using generative AI to turn natural language questions into instant insights and narrative reports. This shift cuts analysis time, improves collaboration, and empowers non-technical teams-but requires strong data governance and human oversight to avoid errors.
How to Validate a SaaS Idea with Vibe Coding for Under $200
Learn how to validate a SaaS idea using AI-powered vibe coding tools for under $200 in 2025. No coding skills needed. Real examples, real costs, real results.
