Author

AI Engineer Programming

In the AI era, defining problems is often more important than solving them; here we explore AI's contradictions, boundaries, and possibilities.

149

Articles

Likes

1.7k

Views

Comments

Latest from AI Engineer Programming

100 recent articles max

AI Engineer Programming

Jun 7, 2026 · Artificial Intelligence

Why Intent Recognition Is the Decision Hub of Agentic AI Systems

The article explains how intent recognition has evolved from simple keyword matching to a central decision hub in Agentic AI, covering basic concepts, LLM and small‑model solutions, hybrid architectures, clarification and out‑of‑scope handling, multi‑turn challenges, routing, evaluation methods, and best‑practice recommendations.

Agentic AIClarificationHybrid Architecture

0 likes · 14 min read

Why Intent Recognition Is the Decision Hub of Agentic AI Systems

AI Engineer Programming

Jun 6, 2026 · Artificial Intelligence

How Query Rewriting Boosts Retrieval in RAG Systems

In RAG applications, ambiguous user queries often hinder retrieval effectiveness, so rewriting queries before search—through normalization, synonym expansion, linguistic rules, LLM‑based generation, query decomposition, and multi‑view strategies—can improve relevance, but must avoid over‑expansion, semantic drift, and added latency.

LLMQuery RewritingRAG

0 likes · 11 min read

How Query Rewriting Boosts Retrieval in RAG Systems

AI Engineer Programming

Jun 5, 2026 · Artificial Intelligence

Multi‑Hop Reasoning vs Document Parsing: Comparing GraphRAG, LightRAG, AgenticRAG and RAGFlow

The article analyzes the classic vector RAG pipeline, highlights its shortcomings for multi‑hop reasoning and global theme inference, and then systematically compares four open‑source frameworks—GraphRAG, LightRAG, AgenticRAG and RAGFlow—detailing their design choices, processing stages, trade‑offs, limitations, and practical selection guidance for production use.

AgenticRAGGraphRAGLightRAG

0 likes · 17 min read

Multi‑Hop Reasoning vs Document Parsing: Comparing GraphRAG, LightRAG, AgenticRAG and RAGFlow

AI Engineer Programming

Jun 3, 2026 · Artificial Intelligence

Production-Grade Agent Memory: Compaction, Decay, and the Observation Engine

The article presents a comprehensive architecture for production‑grade autonomous agents, detailing failure modes, four distinct memory types, a nightly observation engine that turns patterns into procedural rules, tier‑aware decay scoring, context budgeting, GDPR‑compliant deletion, and a step‑by‑step maintenance pipeline.

Agent MemoryGDPR complianceRAG

0 likes · 31 min read

Production-Grade Agent Memory: Compaction, Decay, and the Observation Engine

AI Engineer Programming

Jun 1, 2026 · Artificial Intelligence

Why AI Forgets Your Input and How to Fix It

The article explains that large language models have a limited context window, causing the “lost in the middle” effect where information in the middle of long inputs is ignored, and offers practical strategies such as using larger windows, chunking, summarizing, positioning key data, and caching to mitigate forgetting.

Large Language ModelsRAGcontext window

0 likes · 12 min read

Why AI Forgets Your Input and How to Fix It

AI Engineer Programming

May 30, 2026 · Artificial Intelligence

Should You Pre‑filter or Post‑filter in RAG Vector Search?

The article examines RAG vector retrieval filtering strategies, comparing pre‑filtering (filter before vector search) and post‑filtering (filter after ANN search), and introduces single‑stage filtering, discussing their principles, trade‑offs, suitable scenarios, and architectural implications for accuracy and performance.

ANNRAGmetadata filtering

0 likes · 15 min read

Should You Pre‑filter or Post‑filter in RAG Vector Search?

AI Engineer Programming

May 29, 2026 · Artificial Intelligence

How to Build a Reliable RAG Test Dataset

The article explains why a structured test set is essential for Retrieval‑Augmented Generation systems, outlines failure modes, describes layered evaluation of retrieval and generation, details infrastructure like chunk IDs and manifests, and provides a complete annotation pipeline with cold‑start and adversarial strategies.

LLMRAGadversarial

0 likes · 24 min read

How to Build a Reliable RAG Test Dataset

AI Engineer Programming

May 28, 2026 · Artificial Intelligence

Claude Code Best Practices and Getting Started Guide for Large Codebases

This guide explains how Claude Code can be deployed in massive monorepos, legacy systems, and distributed repositories, detailing navigation methods, the limits of RAG, the benefits of agentic search, and a five‑layer support system—including CLAUDE.md, hooks, skills, plugins, and MCP servers—to help teams of thousands achieve reliable AI‑assisted coding.

AI codingAgentic SearchCLAUDE.md

0 likes · 18 min read

Claude Code Best Practices and Getting Started Guide for Large Codebases

AI Engineer Programming

May 27, 2026 · Artificial Intelligence

MMR for RAG: Low-Cost Chunk Limits Balance Relevance and Diversity

When a long document is split into many highly similar chunks, vector‑based top‑k retrieval tends to return multiple pieces from the same source, causing document dominance; applying a per‑document chunk limit together with Maximal Marginal Relevance (MMR) re‑ranking introduces diversity while preserving relevance, offering a low‑cost way to improve RAG answer quality.

DPPMMRRAG

0 likes · 17 min read

MMR for RAG: Low-Cost Chunk Limits Balance Relevance and Diversity

AI Engineer Programming

May 26, 2026 · Artificial Intelligence

What Exactly Makes a System AI‑Native?

The article defines AI‑native as a system whose existence depends on AI at every layer, contrasts it with AI‑enabled and AI‑first, explains the structural layers, role shifts, bottlenecks, and maturity stages, and offers concrete guidelines for building truly AI‑native engineering practices.

AI-nativeAgenticDevOps

0 likes · 10 min read