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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.

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Latest from AI Engineer Programming

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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.

AgenticRAGGraphRAGKnowledge Graph

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.

RAGcontext windowlarge language models

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.

EvaluationLLMRAG

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.

ChunkingDPPDiversity

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

AI Engineer Programming

May 25, 2026 · Artificial Intelligence

From Demo to Production: Building a Reliable Agent Development Lifecycle

The article outlines a four‑stage agent development lifecycle—Build, Test, Deploy, Monitor—explaining how early, iterative delivery, systematic testing, controlled deployment, and continuous monitoring transform experimental agents into reliable production systems while addressing governance, cost, and scalability challenges.

AgentGovernanceLangChain

0 likes · 16 min read

From Demo to Production: Building a Reliable Agent Development Lifecycle

AI Engineer Programming

May 24, 2026 · Artificial Intelligence

Why AI Agents Fail Beyond Hallucinations

The article catalogs dozens of AI agent failure modes—from one‑shot attempts and cold‑start amnesia to hidden harness control—and explains why these issues quickly overwhelm developers, then outlines concrete mitigation strategies and their trade‑offs.

AI AgentsAgentic Engineeringautomation

0 likes · 11 min read

Why AI Agents Fail Beyond Hallucinations