Articles in this category
All 32 articles, sorted alphabetically
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ARTICLE · 01
Sentence Similarity with BERT and Flask
Read article →ARTICLE · 02
Context Window Wars: How Models Like Gemini Handle 1 Million+ Tokens (And Why It Matters)
Read article →ARTICLE · 03
Data Privacy in the LLM Era: Is Your 'Private' Chat Being Used to Train the Next Model?
Read article →ARTICLE · 04
Direct Preference Optimization (DPO): The New, Simpler Alternative to RLHF
Read article →ARTICLE · 05
Distillation Techniques: How a 'Teacher' LLM Trains a 'Student' SLM to Be Just as Smart
Read article →ARTICLE · 06
Encoder vs. Decoder: Why GPT Chose the Decoder-Only Path While BERT Stayed with the Encoder
Read article →ARTICLE · 07
Evaluating LLM Outputs
LLM-as-judge BLEU/ROUGE limits and human-in-the-loop.
Read article →ARTICLE · 08
FlashAttention Explained
Why the same math runs 2-10x faster.
Read article →ARTICLE · 09
Legal Transformers: Automating Contract Review Without Losing the 'Human in the Loop'
Read article →ARTICLE · 10
LLM Caching Layers
KV cache, prefix cache, semantic cache — all different.
Read article →ARTICLE · 11
LLM Caching Strategies
Exact-match semantic and prompt-prefix caching.
Read article →ARTICLE · 12
LLM Context Management
What goes in, what gets summarized, what gets dropped.
Read article →ARTICLE · 13
LLM Guardrails in Production
Input filtering output validation and PII detection.
Read article →ARTICLE · 14
LLM vs. SLM: When to Choose a 175B Giant Versus a 3B Specialized Assistant
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Medical LLMs: The Ethics and Accuracy of BioGPT and Med-PaLM in Clinical Settings
Read article →ARTICLE · 16
Mixture of Experts in 2026
Mixtral, DeepSeek MoE, and the architecture's tradeoffs.
Read article →ARTICLE · 17
Mixture of Experts (MoE) Explained: How Models Like GPT-4 and Mixtral Use Only 5% of Their Brain at a Time
Read article →ARTICLE · 18
Multi-Modal Tokenization: Processing Sensor Data, Maps, and Audio as Unified Inputs
Read article →ARTICLE · 19
Positional Embeddings: RoPE, ALiBi, and the Quest for Perfect Long-Range Memory
Read article →ARTICLE · 20
Profiling Vram Usage In Transformers
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Prompt Engineering Patterns
Zero-shot few-shot chain-of-thought and reasoning prompts.
Read article →ARTICLE · 22
Retrieval-Augmented Generation (RAG): Bridging the Gap Between a Model’s Training and Today’s News
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Reducing LLM Hallucinations
RAG grounding citation requirements and verification chains.
Read article →ARTICLE · 24
Reinforcement Learning from Human Feedback (RLHF): The Secret Sauce That Made ChatGPT 'Helpful'
Read article →ARTICLE · 25
Rotary Position Embeddings (RoPE)
Why every modern LLM uses it.
Read article →ARTICLE · 26
Speculative Decoding: How Using a Tiny Model to 'Guess' Makes the Big Model 3x Faster
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Structured Output with LLMs
JSON schema function calling and constrained decoding.
Read article →ARTICLE · 28
The Hallucination Problem: Why LLMs Lie and How 'Fact-Checking' Layers Are Being Built
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The LLM as the Ultimate Compiler: From Natural Language to Executable Code
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Understanding Tokenization: Why 'Apple' Is One Token But 'antidisestablishmentarianism' Is Many
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Vision-Language Models (VLM): How Transformers 'See' and Describe Images in Real-Time
Read article →ARTICLE · 32