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.
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.
How Vocabulary Size in Large Language Models Affects Accuracy and Performance
Vocabulary size in large language models directly impacts accuracy, efficiency, and multilingual performance. Learn how tokenization choices affect real-world AI behavior and what size works best for your use case.
Keyboard and Screen Reader Support in AI-Generated UI Components
AI-generated UI components can improve accessibility, but only if they properly support keyboard navigation and screen readers. Learn how current tools work, where they fail, and how to ensure real accessibility-not just automated checks.
Memory and Compute Footprints of Transformer Layers in Production LLMs
Transformer layers in production LLMs consume massive memory and compute, with KV cache now outgrowing model weights. Learn how to identify memory-bound vs. compute-bound workloads and apply proven optimizations like FlashAttention, INT8 quantization, and SwiftKV to cut costs and latency.
Latency and Cost as First-Class Metrics in LLM Evaluation: Why Speed and Price Matter More Than Ever
Latency and cost are now as critical as accuracy in LLM evaluation. Learn how top companies measure response time, reduce token costs, and avoid hidden infrastructure traps in production deployments.
How to Use Large Language Models for Literature Review and Research Synthesis
Learn how to use large language models like GPT-4 and LitLLM to cut literature review time by up to 92%. Discover practical workflows, tools, costs, and why human verification still matters.
AI Ethics Frameworks for Generative AI: Principles, Policies, and Practice
AI ethics frameworks for generative AI must move beyond vague principles to enforceable policies. Learn how top organizations are reducing bias, ensuring transparency, and holding teams accountable-before regulation forces their hand.
Reasoning in Large Language Models: Chain-of-Thought, Self-Consistency, and Debate Explained
Chain-of-Thought, Self-Consistency, and Debate are three key methods that help large language models reason through problems step by step. Learn how they work, where they shine, and why they’re transforming AI in healthcare, finance, and science.
Self-Attention and Positional Encoding: How Transformers Power Generative AI
Self-attention and positional encoding are the core innovations behind Transformer models that power modern generative AI. They enable models to understand context, maintain word order, and generate coherent text at scale.