Tag
AI Engineering
English articles and guides tagged AI Engineering.
Quantitative trading system development record (7): AI engineering implementation - from speckit to BMAD
Taking the trading calendar and daily aggregation requirements as a single case, explain how AI engineering can enter the delivery of real quantitative systems through specification drive, BMAD role handover and manual quality gate control.
Spring AI and LangChain4j: Enterprise Java AI Applications and AI Agent Architecture
A production-grade guide to Spring AI, LangChain4j, RAG, tool calling, memory, governance, observability, reliability, security, and enterprise AI operating boundaries.
From delivery to training: How to turn AI programming collaboration into a Coding Mentor data closed loop
The real organizational value of AI programming assistants is not just to increase delivery speed, but to precipitate trainable, evaluable, and reusable mentor signals in every requirement disassembly, code generation, review and revision, test verification, and online review. This article reconstructs the closed-loop framework of AI training, AI-assisted product engineering delivery, high-quality SFT data precipitation, and model evaluation.
Agent Harness is not a supporting role, but the most underrated main battleground of AI engineering in 2026
What really determines the upper limit of an agent is often not the model itself, but the harness organized around the model. This article is based on LangChain's disassembly of the agent harness, extending my complete understanding of file systems, code execution, context management, verification closed loops and long-term task endurance. It also explains why the focus of AI engineering competition in 2026 is shifting from 'model capabilities' to 'working system design'.
Original interpretation: The art of LLM fine-tuning—from data preparation to model refinement
In-depth exploration of the complete practical path of fine-tuning large language models, from engineering thinking in data preparation to detailed control of model training, reveals the key methodologies that turn general AI into domain experts.
Original interpretation: In-depth analysis of AI Agent system failure modes
Failure mode analysis based on practical experience of multi-Agent systems, combined with predictive thinking from science fiction literature
AI engineering implementation practice map
A practical guide to AI engineering around BMAD, Speckit, and spec-driven development.