Category: Artificial Intelligence - Page 3
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.
Data Residency Considerations for Global LLM Deployments
Data residency for global LLM deployments ensures personal data stays within legal borders. Learn how GDPR, PIPL, and other laws force companies to choose between cloud AI, hybrid systems, or local small models-and the real costs of each.
Citations and Sources in Large Language Models: What They Can and Cannot Do
LLMs can generate convincing citations, but most are fake. Learn why AI hallucinates sources, how to spot them, and what you must do to avoid being misled by AI-generated references in research.
Fine-Tuning for Faithfulness in Generative AI: Supervised and Preference Approaches
Fine-tuning generative AI for faithfulness reduces hallucinations by preserving reasoning integrity. Supervised methods are fast but risky; preference-based approaches like RLHF improve trustworthiness at higher cost. QLoRA offers the best balance for most teams.
Continuous Security Testing for Large Language Model Platforms: Protect AI Systems from Real-Time Threats
Continuous security testing for LLM platforms detects real-time threats like prompt injection and data leaks. Unlike static tests, it runs automatically after every model update, catching vulnerabilities before attackers exploit them.