Articles & Tutorials
149 technical articles covering AI, Machine Learning, Cybersecurity, Data Science, and Programming. Practical guides, tutorials, and deep dives — written for developers.
88 articles · LLMs
Why the Agent Loop Matters
KEY TAKEAWAY --> An AI agent loop is a repeating cycle: Plan → Act → Observe → Evaluate → Decide (repeat or finish). This is the fundame…
The Four Stages
Key Takeaway --> 🎯 Key Takeaway AI coding tools evolved through four stages: autocomplete finishes your lines, chat answers questions, as…
The Complete Loop
Key Takeaway --> 🎯 Key Takeaway Function calling lets an LLM decide which external tool to use and what arguments to pass — but the appli…
Free-Form vs Structured Output
Key Takeaway --> 🎯 Key Takeaway LLMs produce free-form text by default. To build reliable applications, you need structured output: valid…
Why "Looks Good" Is Never Enough
Key Takeaway --> 🎯 Key Takeaway Evaluating a RAG system requires measuring retrieval quality, answer faithfulness, and system performance…
Why RAG Exists: The Hallucination Problem
RAG combines document retrieval with LLM generation. Instead of asking the model to "remember" everything, you search your documents first,…
The Core Comparison
Key Takeaway Prompt engineering controls what you ask. Context engineering controls what the model sees. Your prompt is 0.04% of what the m…
Prompt Engineering vs Context Engineering
Key Takeaway Prompt engineering controls what you ask. Context engineering controls what the model sees. When a 6-token user message trigge…
Category 1: Foundational Patterns
Key Takeaway Prompt patterns are reusable templates for common AI tasks. Mastering 15 core patterns covers 90% of real-world use cases. Eac…
What Are Tokens?
Key Takeaway Every LLM interaction has a finite context window — a fixed budget of tokens for both input and output. More context is not au…
What Are Embeddings?
Key Takeaway Embeddings convert text into numerical vectors where meaningful relationships become mathematical distances. Words with simila…
The Sequence Modeling Problem
Key Takeaway Transformers process all tokens simultaneously using self-attention — a mechanism that lets every token compute how much it sh…