Tag: large language models

Feb, 2 2026

How Curriculum and Data Mixtures Speed Up Large Language Model Scaling

Emily Fies

Smart data ordering and mixtures can boost LLM performance by up to 15% without larger models. Learn how curriculum learning works, what mixtures to use, and whether it’s worth the effort for your team.

Jan, 23 2026

Why Transformers Power Modern Large Language Models: The Core Concepts You Need

Emily Fies

Transformers revolutionized AI by letting language models understand context instantly. Learn how self-attention, positional encoding, and multi-head attention power today’s top LLMs - and why they’re replacing older models.

Jan, 23 2026

Why Transformers Power Modern Large Language Models: The Core Concepts You Need

Emily Fies

Transformers revolutionized AI by enabling large language models to understand context across long texts using self-attention. This article explains how they work, why they beat older models, and what’s changing in 2025.

Jan, 17 2026

Domain Adaptation in NLP: How to Fine-Tune Large Language Models for Medical, Legal, and Financial Text

Emily Fies

Learn how to fine-tune large language models for medical, legal, and financial text using domain adaptation. Discover methods, costs, pitfalls, and real-world results.

Jan, 7 2026

Structured vs Unstructured Pruning for Efficient Large Language Models

Emily Fies

Structured and unstructured pruning help shrink large language models for faster, cheaper deployment. Structured pruning works on any device; unstructured offers higher compression but needs special hardware. Here's how to choose the right one.

Oct, 16 2025

Why Large Language Models Excel at Many Tasks: Transfer, Generalization, and Emergent Abilities

Emily Fies

Large language models excel because they learn from massive text data, then adapt to new tasks with minimal examples. Transfer learning, generalization, and emergent abilities make them powerful without needing custom training for every job.

Sep, 22 2025

Why Transformers Replaced RNNs in Modern Language Models

Emily Fies

Transformers replaced RNNs because they process language faster and understand long-range connections better. With self-attention, they handle entire sentences at once-making modern AI possible.

Sep, 15 2025

How Large Language Models Handle What They Don't Know: Communicating Uncertainty

Emily Fies

Large language models often answer confidently even when they're wrong. Learn how knowledge boundaries and uncertainty communication help them admit when they don't know, reducing hallucinations and building trust in real-world applications.

Aug, 4 2025

How RAG Reduces Hallucinations in Large Language Models: Real-World Impact and Measurements

Emily Fies

RAG reduces hallucinations in large language models by grounding answers in trusted sources. Real-world tests show up to 100% reduction in errors for healthcare and legal applications - but only if the data is clean and well-structured.

Tag: large language models

How Curriculum and Data Mixtures Speed Up Large Language Model Scaling

Why Transformers Power Modern Large Language Models: The Core Concepts You Need

Why Transformers Power Modern Large Language Models: The Core Concepts You Need

Domain Adaptation in NLP: How to Fine-Tune Large Language Models for Medical, Legal, and Financial Text

Structured vs Unstructured Pruning for Efficient Large Language Models

Why Large Language Models Excel at Many Tasks: Transfer, Generalization, and Emergent Abilities

Why Transformers Replaced RNNs in Modern Language Models

How Large Language Models Handle What They Don't Know: Communicating Uncertainty

How RAG Reduces Hallucinations in Large Language Models: Real-World Impact and Measurements

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Measuring Data Quality for LLM Training: Model-Based and Heuristic Filters

How Curriculum and Data Mixtures Speed Up Large Language Model Scaling

Version Control with AI: Managing AI-Generated Commits and Diffs

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