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.
Fixing Insecure AI Patterns: Sanitization, Encoding, and Least Privilege
AI systems are vulnerable to data leaks and attacks through poor output handling. Learn how sanitization, encoding, and least privilege stop breaches before they happen-backed by real incidents and 2025 security standards.
Selecting Open-Source LLMs: Llama, Mistral, Qwen, and DeepSeek Compared
Compare Llama 4, Mistral Large, Qwen 3, and DeepSeek R1 to choose the right open-source LLM for your needs-whether it's multilingual support, reasoning, compliance, or cost. Learn what actually works in 2026.
Domain-Driven Design with Vibe Coding: Master Bounded Contexts and Ubiquitous Language
Domain-Driven Design with Vibe Coding combines strategic architecture principles with AI-assisted development to build scalable, maintainable systems. Learn how Bounded Contexts and Ubiquitous Language prevent code chaos and enable teams to scale AI-powered development safely.
Latency Optimization for Large Language Models: Streaming, Batching, and Caching
Learn how streaming, batching, and caching can slash LLM response times by up to 70%. Real-world benchmarks, hardware tips, and step-by-step optimization for chatbots and APIs.
How to Communicate Confidence and Uncertainty in Generative AI Outputs to Prevent Misinformation
Generative AI often answers with false confidence, leading to misinformation. Learn how to communicate uncertainty in AI outputs using proven methods like text size and simple labels to build trust and prevent harmful errors.
Encoder-Decoder vs Decoder-Only Transformers: Which Architecture Powers Today’s Large Language Models?
Encoder-decoder and decoder-only transformers power today's large language models in different ways. Decoder-only models dominate chatbots and general AI due to speed and scalability, while encoder-decoder models still lead in translation and summarization where precision matters.
How to Build a Coding Center of Excellence: Charter, Staffing, and Goals
Learn how to build a Coding Center of Excellence that actually gets adopted-through a clear charter, the right team structure, and measurable goals that reduce bugs and speed up development.
Inclusive Prompt Design for Diverse Users of Large Language Models
Inclusive prompt design ensures large language models work for everyone-not just fluent English speakers. Learn how IPEM improves accuracy, reduces frustration, and expands access for diverse users across cultures, languages, and abilities.
When to Rewrite AI-Generated Modules Instead of Refactoring
AI-generated code often works-but not well. Learn when to rewrite instead of refactoring to avoid technical debt, security risks, and wasted effort. Data-driven guidelines for smarter decisions.
Economic Impact of Vibe Coding: How AI-Powered Development Is Reshaping Software Costs and Competition
Vibe coding slashes software development costs by up to 85% but increases long-term maintenance expenses. Learn how AI-powered development is reshaping competition, skills, and economic risks in 2026.
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.