Author: Collin Pace
Energy Efficiency in Generative AI Training: Sparsity, Pruning, and Low-Rank Methods
Sparsity, pruning, and low-rank methods slash generative AI training energy by 40-80% without sacrificing accuracy. Learn how these techniques work, their real-world results, and why they're becoming mandatory for sustainable AI.
- Dec 17, 2025
- Collin Pace
- 3
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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.
- Dec 8, 2025
- Collin Pace
- 2
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How to Reduce Memory Footprint for Hosting Multiple Large Language Models
Learn how to reduce memory footprint when hosting multiple large language models using quantization, model parallelism, and hybrid techniques. Cut costs by 65% and run 3-5 models on a single GPU.
- Nov 29, 2025
- Collin Pace
- 2
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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.
- Nov 29, 2025
- Collin Pace
- 3
- Permalink
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.
- Nov 24, 2025
- Collin Pace
- 2
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Build vs Buy for Generative AI Platforms: Decision Framework for CIOs
CIOs must choose between building or buying generative AI platforms based on cost, speed, risk, and use case. Learn the three strategies - buy, boost, build - and which one fits your organization.
- Nov 14, 2025
- Collin Pace
- 3
- Permalink
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.
- Oct 20, 2025
- Collin Pace
- 1
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Model Lifecycle Management: Versioning, Deprecation, and Sunset Policies Explained
Learn how versioning, deprecation, and sunset policies keep AI models reliable, compliant, and safe. Real-world examples, industry standards, and actionable steps for managing AI lifecycles.
- Sep 24, 2025
- Collin Pace
- 0
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Top Enterprise Use Cases for Large Language Models in 2025
In 2025, enterprise LLMs are transforming customer service, compliance, fraud detection, and document processing. Discover the top real-world use cases driving ROI, the critical factors for success, and why security and integration matter more than model size.
- Sep 19, 2025
- Collin Pace
- 2
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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.
- Sep 16, 2025
- Collin Pace
- 0
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How to Use Large Language Models for Marketing, Ads, and SEO
Learn how to use large language models for marketing, ads, and SEO without falling into common traps like hallucinations or lost brand voice. Real strategies, real results.
- Sep 5, 2025
- Collin Pace
- 4
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Continuous Documentation: Keep Your READMEs and Diagrams in Sync with Your Code
Keep your READMEs and diagrams accurate by syncing them with your codebase using automation tools like GitHub Actions, ReadMe.io, and DeepDocs. Stop manual updates. Start living documentation.
- Aug 31, 2025
- Collin Pace
- 1
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