AI Articles: Tutorials, Reviews and In-depth Guides

How-toEvaluation

How to Evaluate a Large Language Model: Scoring Dimensions and Prompt Templates

Don't rely solely on leaderboards—public rankings may have little relevance to your actual use cases. This article provides a hands-on framework for evaluating large language models, covering how to build evaluation datasets, define scoring dimensions, key considerations when using LLMs as judges, and a ready-to-use prompt template for scoring.

AI Beyond Editor16
InsightsKnowledge Base

Why Do AI Models Hallucinate? From Probabilistic Prediction to Engineering Constraints

Hallucinations are not bugs but an inevitable byproduct of the 'predict-the-next-token' mechanism. This article explains, from first principles, why models confidently generate falsehoods, then examines four engineering approaches—retrieval, constraints, verification, and refusal—to clarify why hallucinations cannot be eradicated entirely, only managed.

AI Beyond Editor4
InsightsKnowledge Base

Understanding How Embeddings Enable Semantic Search in Just 20 Lines of Code

Keyword search matches literal terms, while semantic search grasps meaning—the magic lies in embeddings. This article walks through a minimal code example that transforms text into vectors, compares them by distance, and equates proximity with similarity, clarifying the principles, limitations, and common misconceptions behind semantic search.

AI Beyond Editor30
How-toEvaluation

What Does Temperature Actually Change? Sampling Theory and Practical Settings

Temperature is not a 'creativity knob' but a parameter that controls randomness during sampling. Starting from the fact that a model's next token is a probability distribution, this guide explains what temperature and top-p change, how to set them for different tasks, and how to run a reproducible comparison.

AI Beyond Editor14
Head-to-headReviews

LLM Model Selection Review for Engineers: Reasoning, Coding, Long Context, and Cost

A model-layer selection guide comparing the primary models from OpenAI, Anthropic, Google, and DeepSeek across reasoning, coding, context window, Chinese language proficiency, and cost. This round covers GPT-4o, Claude 4 Opus, Gemini 2.5 Pro, and DeepSeek R1, verified on 2026-07-01.

AI Beyond Editor495
InsightsCoding

Why Cursor Is Taking Off: A Product Breakdown from AI Autocomplete to Agent-Based Programming

What sets Cursor apart among the crowded field of AI coding tools? This article dissects its product logic: why it was built as an 'AI-native IDE' rather than a plugin, how Tab completion and Composer/Agent roles are divided, the role of codebase indexing, and where it diverges from pure autocomplete tools or terminal-based agent solutions.

AI Beyond Editor9

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