Tag: large language models

Oct, 4 2026

Self-Attention in Transformers: How LLMs Understand Context

Emily Fies

Discover how self-attention powers Large Language Models. Learn the QKV framework, multi-head mechanics, and why this 2017 innovation revolutionized AI understanding.

Oct, 1 2026

Context Windows in LLMs: Limits, Trade-Offs & Best Practices

Emily Fies

Explore how LLM context windows work, from token limits to hardware constraints. Learn why bigger isn't always better and discover best practices for managing long conversations and documents efficiently.

Sep, 29 2026

Document Re-Ranking: Boosting RAG Relevance for LLMs

Emily Fies

Boost RAG accuracy with document re-ranking. Learn how cross-encoders solve vector search limitations, improve LLM factuality, and optimize retrieval pipelines.

Sep, 18 2026

Multi-Turn Conversations with LLMs: Managing Conversation State

Emily Fies

Discover why LLMs fail in multi-turn chats and how to fix it. Learn about loss masking, Review-Instruct, and strategies to manage conversation state for reliable AI.

Aug, 9 2026

How Autoregressive Generation Works: Step-by-Step Token Production in LLMs

Emily Fies

Explore how autoregressive generation works in large language models. Learn the step-by-step process of token production, causal masking, and the limitations of sequential AI text generation.

Aug, 7 2026

How Think-Tokens Change Generation: Reasoning Traces in Modern Large Language Models

Emily Fies

Explore how think-tokens and reasoning traces transform LLM generation, boosting accuracy by 37% while adding latency. Learn the mechanics, efficiency trade-offs, and optimization strategies for modern AI models.

Jul, 30 2026

How Large Language Models Use Probabilities to Choose Words and Phrases

Emily Fies

Explore how Large Language Models use conditional probability, softmax functions, and decoding strategies like top-p and temperature to choose words. Learn why this statistical approach leads to both impressive creativity and occasional hallucinations.

Jul, 18 2026

Causal vs Bidirectional Attention: Tradeoffs in Modern LLMs

Emily Fies

Explore the critical tradeoffs between causal and bidirectional attention in modern LLMs. Learn how these mechanisms impact performance, speed, and suitability for different AI tasks.

Jun, 28 2026

What Makes a Language Model 'Large': Beyond Parameter Counts and Into Capabilities

Emily Fies

Explore what truly makes a language model 'large' in 2026. From emergent capabilities to Virtual Logical Depth, discover why parameter counts no longer define AI performance.

Jun, 22 2026

Hybrid Recurrent-Transformer Models: Do They Actually Help LLMs?

Emily Fies

Explore how hybrid recurrent-transformer models combine Mamba and attention to solve LLM scaling issues. Learn about sequential vs. parallel designs, real-world examples like Hunyuan-TurboS, and performance trade-offs.

Jun, 10 2026

Semantic Search with LLMs: How AI Transforms Keyword Matching into Intent Understanding

Emily Fies

Discover how Large Language Models transform search from keyword matching to intent understanding. Learn about vector embeddings, query expansion, and re-ranking strategies for building smarter, semantic search systems.

Mar, 27 2026

Robustness and Generalization Tests for Large Language Model Reliability

Emily Fies

Learn essential robustness testing methods for LLMs beyond standard benchmarks, including adversarial stress tests, OOD validation, and real-world deployment readiness.

Tag: large language models

Self-Attention in Transformers: How LLMs Understand Context

Context Windows in LLMs: Limits, Trade-Offs & Best Practices

Document Re-Ranking: Boosting RAG Relevance for LLMs

Multi-Turn Conversations with LLMs: Managing Conversation State

How Autoregressive Generation Works: Step-by-Step Token Production in LLMs

How Think-Tokens Change Generation: Reasoning Traces in Modern Large Language Models

How Large Language Models Use Probabilities to Choose Words and Phrases

Causal vs Bidirectional Attention: Tradeoffs in Modern LLMs

What Makes a Language Model 'Large': Beyond Parameter Counts and Into Capabilities

Hybrid Recurrent-Transformer Models: Do They Actually Help LLMs?

Semantic Search with LLMs: How AI Transforms Keyword Matching into Intent Understanding

Robustness and Generalization Tests for Large Language Model Reliability

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Context Windows in LLMs: Limits, Trade-Offs & Best Practices

Self-Attention in Transformers: How LLMs Understand Context

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