Large Language Models: What They Can Do and How to Use Them Responsibly
When you use a large language model, an AI system trained to understand and generate human-like text. Also known as LLMs, they power everything from chatbots to code assistants—but they don’t think like people. They predict words, not truths. That’s why LLM security, the practice of protecting AI systems from manipulation like prompt injection and data leaks matters just as much as accuracy. And when AI ethics, the framework guiding fair, transparent, and accountable AI use is ignored, even the best models can cause real harm.
Most teams focus on speed and cost, but the real challenge is trust. Can you rely on citations? Do you know if your model remembers private data? Can a smaller model reason as well as a giant one? The posts below answer these questions with real examples—from how companies cut LLM costs by 80% using prompt compression, to why checkpoint averaging now saves teams weeks of training time. You’ll find practical guides on LLMs in business, how to stop hallucinated sources, and what actually works for making AI feel trustworthy to users.
What follows isn’t theory. It’s what’s working right now—for researchers, developers, and teams building AI that doesn’t just impress, but delivers.
Beyond BLEU and ROUGE: Why Semantic Metrics Are the New Standard for LLM Evaluation
BLEU and ROUGE are outdated for evaluating modern LLMs. Semantic metrics like BERTScore and BLEURT measure meaning, not word overlap, and correlate far better with human judgment. Here's how to use them effectively.
KPIs and Dashboards for Monitoring Large Language Model Health
Learn the essential KPIs and dashboard practices for monitoring large language model health in production. Track hallucinations, cost, latency, and safety to prevent failures and maintain user trust.
Teaching LLMs to Say 'I Don’t Know': Uncertainty Prompts That Reduce Hallucination
Learn how to reduce LLM hallucinations by teaching models to say 'I don't know' using uncertainty prompts and structured training methods like US-Tuning - proven to cut false confidence by 67% in real-world applications.
Clean Architecture in Vibe-Coded Projects: How to Keep Frameworks at the Edges
Clean architecture in vibe-coded projects keeps AI-generated code from tainting your core logic with framework dependencies. Learn how to enforce boundaries, use tools like Sheriff, and build maintainable apps faster.
Implementing Generative AI Responsibly: Governance, Oversight, and Compliance
Learn how to implement generative AI responsibly with governance, oversight, and compliance frameworks that prevent legal risks, bias, and reputational damage. Real-world strategies for 2026.
Framework-Aligned Vibe Coding with Wasp for Full-Stack Apps
Wasp is a declarative full-stack framework that generates React, Node.js, and PostgreSQL code from a simple config file, cutting development time by 60-70%. Ideal for MVPs and internal tools.
Real-Time Multimodal Assistants Powered by Large Language Models: What They Can Do Today
Real-time multimodal assistants powered by large language models can see, hear, and respond instantly to text, images, and audio. Learn how GPT-4o, Gemini 1.5 Pro, and Llama 3 work today-and where they still fall short.
Security for RAG: How to Protect Private Documents in Large Language Model Workflows
Learn how to protect private documents in RAG systems using multi-layered security, encryption, access controls, and real-world best practices to prevent data leaks in enterprise AI workflows.
Trustworthy AI for Code: How Verification, Provenance, and Watermarking Are Changing Software Development
AI-generated code is everywhere-but without verification, provenance, and watermarking, it’s a ticking time bomb. Learn how trustworthy AI for code is changing software development in 2026.
Prompting as Programming: How Natural Language Became the Interface for LLMs
Natural language is now the primary way humans interact with AI. Prompt engineering turns simple text into powerful programs, replacing code for many tasks. Learn how it works, why it's changing development, and how to use it effectively.
Secure Human Review Workflows for Sensitive LLM Outputs
Human review workflows are essential for securing sensitive LLM outputs in regulated industries. Learn how to build a compliant, scalable system that prevents data leaks and meets GDPR and HIPAA requirements.
Board-Level Briefing: Strategic Implications of Vibe Coding for 2026
Vibe coding lets non-engineers build software with AI-but at what cost? Boards must understand its risks: hidden tech debt, legal liability, and system failures. Here’s how to use it safely in 2026.