AI Articles: Tutorials, Reviews and In-depth Guides

InsightsAgents

ReAct vs. Plan-and-Execute vs. Reflection: Three Agent Patterns Explained

ReAct thinks and acts one step at a time, Plan-and-Execute plans before moving, and Reflection checks the work afterward. This guide explains the problem each common Agent pattern solves, the cost it introduces, and how to combine the three in practice.

AI Beyond Editor23
How-toKnowledge Base

How Does AI Read a PDF? From OCR and Parsing to RAG Q&A

PDF is one of the hardest inputs for RAG: scans require OCR, tables get flattened, and multi-column layouts scramble reading order. This guide breaks the workflow into four stages—extract text, preserve structure, chunk and index, then answer questions—and explains the pitfalls and tool choices at each step.

AI Beyond Editor24
InsightsEvaluation

Are Long-Context Models Truly Superior? A Practical Analysis of Failure Scenarios

While context windows have expanded from thousands to millions of tokens, the ability to 'hold' more data does not guarantee it is used effectively. This article clarifies three real-world failure modes of long-context models: ignoring information in the middle of a sequence, reduced accuracy due to distracting content, and soaring costs and latency. It also outlines when to leverage long contexts versus when Retrieval-Augmented Generation (RAG) is the better choice.

AI Beyond Editor8
InsightsKnowledge Base

What Is Reranking? Why It's Often Essential for RAG

Recall ensures nothing is missed; reranking ensures the right order. This article clarifies the fundamental differences between Cross-Encoder reranking and vector retrieval, explains when adding a reranker is worthwhile, guides model selection, and provides a ready-to-use two-stage retrieval configuration.

AI Beyond Editor9
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 Beyond Editor17
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 Beyond Editor25
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 Beyond Editor15

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