AI Articles: Tutorials, Reviews and In-depth Guides

Illustration of document segmentation with annotations
How-toKnowledge Base

How to Split Documents for RAG? A Practical Comparison of Chunk Sizes

Document splitting is the most cost-effective yet often overlooked step in RAG. This article compares four strategies—fixed-length, recursive, structured, and semantic chunking—and evaluates their performance across different chunk sizes, offering a ready-to-use default configuration and an optimization workflow.

AI Article Editorial Team17
A data center illustrating the retrieval pipeline
InsightsKnowledge Base

Why Does RAG Give Wrong Answers? 10 Engineering Causes and Fixes

When RAG gives incorrect or off-target answers, the model usually is not the problem—the retrieval pipeline is. This guide identifies 10 common engineering causes across ingestion, retrieval, and generation, with practical fixes and ways to validate each one.

AI Content Editor25
An abstract illustration of retrieval and matching
How-toKnowledge Base

Vector Search, BM25, or Hybrid Search: Which Should You Choose?

Vector search understands meaning but struggles with model numbers and identifiers. BM25 matches exact words but misses paraphrases. Hybrid search runs both and combines the results. This guide explains their tradeoffs, reciprocal rank fusion, and which approach fits each use case.

AI Content Editor15
Illustration of scoring dimensions and data dashboard
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 Article Editorial Team16
Abstract illustration of neural networks and uncertainty
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 Article Editorial Team4
Illustration of vector space and semantic distance
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 Article Editorial Team30
A probability distribution and sampling process
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 Content Editor14

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