Hualin Luan Cloud Native · Quant Trading · AI Engineering

Topic

AI engineering practice

An engineering path from prototype to production for large model applications, covering core topics such as Prompt Engineering, RAG Architecture, LLM Ops, and more.

AI engineering practice focuses on how to systematically integrate large model capabilities into software engineering to achieve deployable, maintainable, and scalable AI applications.

core concerns

  • Prompt version management and testing: How to manage complex Prompt templates to achieve A/B testing and version rollback
  • RAG System Performance Optimization: From simple vector retrieval to production-level hybrid retrieval strategies
  • LLM Application Monitoring and Observability: Track requests, assess output quality, monitor costs
  • Multi-model routing and degradation strategy: Select the optimal model according to the scenario to achieve elegant fault degradation

Practical methods

This topic uses the BMAD (Brainstorm-Map-Architect-Develop) method, combined with the Speckit specification-driven development process, to demonstrate the entire process of AI-assisted software engineering.

Index

Knowledge Index

Core subtopics and learning directions for this topic.

Prompt EngineeringRAG systemLLM OpsAI application architecturemulti-model routing

Reading paths

Start Here

Follow the curated path first when you need an ordered mental model.

Path

AI engineering practice

View topic →

An engineering path from prototype to production for large model applications, covering core topics such as Prompt Engineering, RAG Architecture, LLM Ops, and more.

  1. 1. AI engineering delivery practice map

    guide

    A navigation guide for AI engineering delivery: RAG knowledge entry points, requirement contracts, AI-TDD Manifest, BMAD/Speckit workflows, delivery gates, and reusable evidence.

  2. 2. Building a RAG System: Architecture, Retrieval, Quality, and Release Gates

    guide

    A three-part guide for building a knowledge hub RAG assistant, from system boundaries to retrieval implementation, quality evaluation, safety controls, and release evidence.

  3. 3. AI-TDD: Requirement Contracts and Multi-Dimensional Evidence Acceptance

    post

    AI-TDD turns human intent into a Manifest requirement contract, then accepts AI-generated work through evidence chains and Gate verdicts.

  4. 4. Technical Interpretation Index | Curated Translations

    post

    Original technical interpretation and selected articles from foreign technology communities to explore best practices in AI engineering

  5. 5. Original interpretation: Discovery and prevention of silent hallucination in RAG system

    post

    Based on an in-depth analysis of RAG system failure cases in the production environment, we explore the nature of the silent illusion problem, monitoring blind spots, and architectural-level solutions.

  6. 6. Original interpretation: How AI Agent implements large-scale testing quality access control

    post

    Practical analysis of AI testing agent based on Node.js project scaffolding, and explore the implementation ideas of automated quality access control

  7. 7. RAG Retrieval Implementation Deep Dive: Chunking, Hybrid Retrieval, and Intent Routing

    post

    An implementation guide for chunking, stable chunk IDs, hybrid retrieval, rule-based intent routing, current-page summaries, rerank, fallback, and verification.

Guides

Guides in this topic

Evergreen guides that complement the ordered reading path.

Guide AI engineering practice Guide 1

AI engineering delivery practice map

A navigation guide for AI engineering delivery: RAG knowledge entry points, requirement contracts, AI-TDD Manifest, BMAD/Speckit workflows, delivery gates, and reusable evidence.

Rag Bmad Speckit Ai Tdd AI Engineering Manifest Driven Spec Driven Development
Guide AI engineering practice Guide 2

Building a RAG System: Architecture, Retrieval, Quality, and Release Gates

A three-part guide for building a knowledge hub RAG assistant, from system boundaries to retrieval implementation, quality evaluation, safety controls, and release evidence.

RAG Knowledge Hub Cloudflare Hybrid Retrieval Quality Gates

Articles

More Articles

Additional topic articles that are not already highlighted in Start Here, Series, or Guides.

Article AI engineering practice 5/29/2026

RAG Quality Evaluation and Safety Controls: From Rule-Based Evaluation to Release Gates

A release-quality guide for retrieval, citation, and answer evaluation, six safety layers, privacy-aware telemetry, and public RAG launch gates.

RAG Quality Evaluation Safety Controls Rule Based Governance
Article AI engineering practice 5/29/2026

RAG System Architecture: Edge Runtime, Hybrid Retrieval, and Incremental Indexing

A practical architecture guide for a knowledge hub RAG assistant using Cloudflare Workers, Vectorize, D1 FTS5, KV, hybrid retrieval, and incremental indexing.

RAG Edge Computing Cloudflare Vector Search Hybrid Retrieval
Article AI engineering practice 3/12/2026

Original interpretation: Agent quality assessment - the cornerstone of trust in the AI ​​era

In-depth analysis of the essential challenges of Agent quality assessment and why quality engineering is the key to determining the success or failure of AI products

Agent Quality Evaluation Framework Llm Judge Ab Testing Original Interpretation
Article AI engineering practice 3/12/2026

Original interpretation: MCP protocol - the USB-C moment of the Agent ecosystem

An in-depth analysis of the essence of the Model Context Protocol protocol design and why standardization is the key to the prosperity of the Agent ecosystem

Mcp Model Context Protocol Agent Tools Interoperability Original Interpretation
Article AI engineering practice 3/12/2026

Original Interpretation: Contextual Engineering—The Forgotten Core Battlefield in the AI ​​Era

An in-depth analysis of the essential challenges of Agent memory systems and why context management is the key to determining the success or failure of AI products.

Context Engineering Agent Memory Llm Ops Production Challenges Original Interpretation
Article AI engineering practice 3/12/2026

Original interpretation: Kaggle white paper "Introduction to Agents" - AI Agent introduction and architecture panorama

In-depth analysis of the five levels, core architecture and production practices of Agent, and sorting out the key framework and inspiration of the Kaggle white paper "Introduction to Agents"

AI Agent LLM Multi Agent System Kaggle Architecture Design Original Interpretation
Article AI engineering practice 3/12/2026

Original interpretation: From prototype to production - the engineering transition of the Agent system

In-depth analysis of the core challenges of Agent production and how to transform Agent prototypes into reliable production-level systems

Agent Production Agentops Ci Cd Production Deployment Multi Agent Systems Original Interpretation
Article AI engineering practice 3/11/2026

Original interpretation: The essential challenge of observability in Agent production environment

An in-depth analysis of the fundamental differences between Agent and traditional software, and why traditional monitoring methods fail in the AI ​​era

Agent Observability Production Monitoring Llm Ops Original Interpretation

Resources

Resources

External references and project resources for this topic.

BMAD method

https://github.com/

LangChain Documentation

https://python.langchain.com/