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AI Engineer Programming
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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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Recent Articles

Latest from AI Engineer Programming

53 recent articles
AI Engineer Programming
AI Engineer Programming
May 9, 2026 · Artificial Intelligence

Why PDF Parsing Is Hard for RAG and Which Mainstream Solutions Work

The article examines the intrinsic challenges of extracting structured text from PDFs for Retrieval‑Augmented Generation—such as missing reading order, table reconstruction, font encoding, and scanned images—and compares lightweight libraries, AI‑enhanced frameworks, commercial APIs, and visual language models as practical solutions.

AI frameworksOCRPDF parsing
0 likes · 23 min read
Why PDF Parsing Is Hard for RAG and Which Mainstream Solutions Work
AI Engineer Programming
AI Engineer Programming
May 8, 2026 · Artificial Intelligence

Is Non-Vector RAG the Next Generation of Retrieval‑Augmented Generation?

The article analyses the relevance and accuracy shortcomings of traditional vector‑based RAG, explains how non‑vector approaches like PageIndex let LLMs navigate document trees for relevance classification and auditability, and evaluates their complexity, latency, metadata risks, and suitable use cases compared with hybrid retrieval.

Hybrid RetrievalLLMRAG
0 likes · 8 min read
Is Non-Vector RAG the Next Generation of Retrieval‑Augmented Generation?
AI Engineer Programming
AI Engineer Programming
May 7, 2026 · Artificial Intelligence

How Cursor Turned Its Coding Agent from Demo to Production

The article examines Cursor's journey of shipping its Composer coding agent, detailing the agentic AI model, system architecture, and the three major production challenges—diff handling, latency accumulation, and sandbox scaling—along with the engineering solutions that enabled reliable, fast, and adoptable AI‑driven code generation.

Agentic AICoding AgentCursor
0 likes · 16 min read
How Cursor Turned Its Coding Agent from Demo to Production
AI Engineer Programming
AI Engineer Programming
May 6, 2026 · Artificial Intelligence

How to Evaluate and Choose Embedding Models for RAG Systems

This article explains why embedding models are the foundation of RAG pipelines, outlines concrete evaluation metrics such as MTEB v2 scores, latency, throughput and cost, compares a range of commercial and open‑source models, and discusses emerging trends like multimodal and long‑context embeddings.

MTEBRAGembedding models
0 likes · 13 min read
How to Evaluate and Choose Embedding Models for RAG Systems
AI Engineer Programming
AI Engineer Programming
May 5, 2026 · Artificial Intelligence

Deep Dive into Agent Harness: Turning LLM Failures into Robust AI Agents

The article dissects the concept of an Agent Harness— the full software infrastructure that wraps LLMs— covering its twelve components, engineering layers, context management, error handling, and validation loops, and explains how proper harness design can prevent common agent failures and dramatically improve performance.

AI AgentsAgent HarnessContext Management
0 likes · 24 min read
Deep Dive into Agent Harness: Turning LLM Failures into Robust AI Agents
AI Engineer Programming
AI Engineer Programming
May 4, 2026 · Artificial Intelligence

RAG in the Long-Context Era: Challenges, Benchmarks, and Context Engineering

The article analyzes how expanding LLM context windows to millions of tokens reshape Retrieval‑Augmented Generation, detailing chunking trade‑offs, embedding retrieval limits, attention U‑shaped distribution, benchmark results, and the emerging practice of Context Engineering for optimal end‑to‑end pipelines.

Embedding RetrievalLLMLong-Context
0 likes · 10 min read
RAG in the Long-Context Era: Challenges, Benchmarks, and Context Engineering
AI Engineer Programming
AI Engineer Programming
May 2, 2026 · Artificial Intelligence

From Demo to Production: How to Evaluate RAG Effectively

This guide outlines a comprehensive RAG evaluation framework covering failure modes, multi‑layer metrics, test‑set construction, open‑source tools, CI/CD quality gates, production monitoring, and special considerations for agentic RAG to ensure reliable, trustworthy retrieval‑augmented generation systems.

AIGenerationLLM
0 likes · 18 min read
From Demo to Production: How to Evaluate RAG Effectively
AI Engineer Programming
AI Engineer Programming
May 1, 2026 · Artificial Intelligence

From Naive Retrieval to Knowledge Runtime: The Full Evolution of RAG

The article traces the evolution of Retrieval‑Augmented Generation from its 2020 Naive baseline through Advanced, Modular, Graph, and Agentic generations, detailing architectural shifts, optimization techniques, self‑correction mechanisms, and future challenges such as long‑context handling and multimodal retrieval.

LLMRAGagentic
0 likes · 14 min read
From Naive Retrieval to Knowledge Runtime: The Full Evolution of RAG
AI Engineer Programming
AI Engineer Programming
Apr 29, 2026 · Information Security

Managing AI Agents Like Engineering Teams: A Five‑Layer Governance Stack

The article presents a five‑layer governance stack for AI agents—identity, centralized tool registry, policy enforcement, behavioral anomaly detection, and unified security posture—detailing how each layer mirrors traditional engineering team management to reduce attack surface, audit complexity, and migration costs.

AI Agentsanomaly detectioncloud security
0 likes · 11 min read
Managing AI Agents Like Engineering Teams: A Five‑Layer Governance Stack