Category: Artificial Intelligence

Evaluation Protocols for Compressed Large Language Models: What Works, What Doesn’t, and How to Get It Right

Evaluation Protocols for Compressed Large Language Models: What Works, What Doesn’t, and How to Get It Right

Compressed LLMs can look perfect on perplexity scores but fail in real use. Learn the three evaluation pillars-size, speed, substance-and the benchmarks (LLM-KICK, EleutherAI) that actually catch silent failures before deployment.

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Citation and Attribution in RAG Outputs: How to Build Trustworthy LLM Responses

Citation and Attribution in RAG Outputs: How to Build Trustworthy LLM Responses

Citation and attribution in RAG systems are essential for trustworthy AI responses. Learn how to implement accurate, verifiable citations using real-world tools, data standards, and best practices from 2025 enterprise deployments.

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Designing Multimodal Generative AI Applications: Input Strategies and Output Formats

Designing Multimodal Generative AI Applications: Input Strategies and Output Formats

Multimodal generative AI lets apps understand and respond to text, images, audio, and video together. Learn how to design inputs that work, choose the right outputs, and use models like GPT-4o and Gemini effectively.

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Transformer Pre-Norm vs Post-Norm Architectures: Which One Powers Modern LLMs?

Transformer Pre-Norm vs Post-Norm Architectures: Which One Powers Modern LLMs?

Pre-Norm and Post-Norm are two ways to structure layer normalization in Transformers. Pre-Norm powers most modern LLMs because it trains stably at 100+ layers. Post-Norm works for small models but fails at scale.

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Contextual Representations in Large Language Models: How LLMs Understand Meaning

Contextual Representations in Large Language Models: How LLMs Understand Meaning

Contextual representations let LLMs understand words based on their surroundings, not fixed meanings. From attention mechanisms to context windows, here’s how models like GPT-4 and Claude 3 make sense of language - and where they still fall short.

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Supervised Fine-Tuning for Large Language Models: A Practical Guide for Real-World Use

Supervised Fine-Tuning for Large Language Models: A Practical Guide for Real-World Use

Supervised fine-tuning turns generic LLMs into reliable tools using real examples. Learn how to do it right with minimal cost, avoid common mistakes, and get real results without needing an AI PhD.

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