VAHU: Visionary AI & Human Understanding - Page 4
Access Control and Authentication Patterns for LLM Services: Secure AI Without Compromising Usability
Learn how to secure LLM services with proper authentication and access control. Discover proven patterns like OAuth2, JWT, RBAC, and ABAC-and avoid the most common mistakes that lead to prompt injection and data leaks.
Continuous Documentation: Keep Your READMEs and Diagrams in Sync with Your Code
Stop wasting time on outdated READMEs and diagrams. Learn how to automate documentation sync with your code using CI/CD tools, AI, and simple workflows - so your docs always match reality.
Prompt Injection Attacks Against Large Language Models: How to Detect and Defend Against Them
Prompt injection attacks trick AI systems into revealing secrets or ignoring instructions. Learn how they work, why traditional security fails, and the layered defense strategy that actually works against this top AI vulnerability.
Legal and Regulatory Compliance for LLM Data Processing in 2025
LLM compliance in 2025 means real-time data controls, not just policies. Understand EU AI Act, California laws, technical requirements, and how to avoid $2M+ fines.
Prompt Length vs Output Quality: The Hidden Cost of Too Much Context in LLMs
Longer prompts don't improve LLM output-they hurt it. Discover why 2,000 tokens is the sweet spot for accuracy, speed, and cost-efficiency, and how to fix bloated prompts today.
How Compression Interacts with Scaling in Large Language Models
Compression and scaling in LLMs don't follow simple rules. Larger models gain more from compression, but each technique has limits. Learn how quantization, pruning, and hybrid methods affect performance, cost, and speed across different model sizes.
Onboarding Developers to Vibe-Coded Codebases: Playbooks and Tours
Vibe coding speeds up development but creates chaotic codebases. Learn how to onboard developers with playbooks, codebase tours, and AI prompt documentation to avoid confusion and burnout.
Toolformer-Style Self-Supervision: How LLMs Learn to Use Tools on Their Own
Toolformer teaches large language models to use tools like calculators and search engines on their own-without human labels. It boosts accuracy in math and facts while keeping language skills intact.
Red Teaming for Privacy: How to Test Large Language Models for Data Leakage
Learn how red teaming exposes data leaks in large language models, why it's now legally required, and how to test your AI safely using free tools and real-world methods.
OCR and Multimodal Generative AI: Extracting Structured Data from Images
Modern OCR powered by multimodal AI can extract structured data from images with 90%+ accuracy, turning messy documents into clean, usable information. Learn how Google, AWS, and Microsoft are changing document processing-and what you need to know before adopting it.
Autonomous Agents Built on Large Language Models: What They Can Do and Where They Still Fail
Autonomous agents built on large language models can plan, act, and adapt without constant human input-but they still make mistakes, lack true self-improvement, and struggle with edge cases. Here’s what they can do today, and where they fall short.
Structured vs Unstructured Pruning for Efficient Large Language Models
Structured and unstructured pruning help shrink large language models for real-world use. Structured pruning keeps hardware compatibility; unstructured gives higher compression but needs special chips. Learn which one fits your needs.