VAHU: Visionary AI & Human Understanding - Page 3
Attribution Challenges in Generative AI ROI: How to Isolate AI Effects from Other Business Changes
Most companies can't prove their generative AI investments pay off-not because the tech fails, but because they can't isolate AI's impact from other changes. Learn how to measure true ROI with real-world methods.
Risk-Based App Categories: How to Classify Prototypes, Internal Tools, and External Products for Better Security
Learn how to classify apps into prototypes, internal tools, and external products based on risk to improve security, save resources, and avoid costly breaches. A practical guide for teams managing multiple applications.
Fine-Tuning for Faithfulness in Generative AI: Supervised and Preference Approaches
Fine-tuning generative AI for faithfulness reduces hallucinations by preserving reasoning integrity. Supervised methods are fast but risky; preference-based approaches like RLHF improve trustworthiness at higher cost. QLoRA offers the best balance for most teams.
Continuous Security Testing for Large Language Model Platforms: Protect AI Systems from Real-Time Threats
Continuous security testing for LLM platforms detects real-time threats like prompt injection and data leaks. Unlike static tests, it runs automatically after every model update, catching vulnerabilities before attackers exploit them.
Governance Models for Generative AI: Councils, Policies, and Accountability
Governance models for generative AI-councils, policies, and accountability-are no longer optional. Learn how leading organizations reduce risk, accelerate deployment, and build trust with real-world frameworks and data from 2025.
Measuring Developer Productivity with AI Coding Assistants: Throughput and Quality
AI coding assistants can boost developer throughput-but only if you track quality too. Learn how top companies measure real productivity gains and avoid hidden costs like technical debt and review bottlenecks.