Category: Artificial Intelligence
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.
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.
Designing Trustworthy Generative AI UX: Transparency, Feedback, and Control
Trust in generative AI comes from transparency, feedback, and control-not flashy interfaces. Learn how leading platforms like Microsoft Copilot and Salesforce Einstein build user trust with proven design principles.
Prompt Compression: Cut Token Costs Without Losing LLM Accuracy
Prompt compression cuts LLM input costs by up to 80% without sacrificing answer quality. Learn how to reduce tokens using hard and soft methods, real-world savings, and when to avoid it.
Checkpoint Averaging and EMA: How to Stabilize Large Language Model Training
Checkpoint averaging and EMA stabilize large language model training by combining multiple model states to reduce noise and improve generalization. Learn how to implement them, when to use them, and why they're now essential for models over 1B parameters.