A good **Spec Coding** (specification-driven programming) is about turning "vague ideas" into "precise, verifiable, and executable contracts." It's not just writing a document, but establishing a set of **unambiguous communication languages** between humans and AI (or between humans). Below, I will …
Although the "feel/atmosphere-driven" mode of Vibe Coding is great for rapid prototyping and creative exploration, if not controlled, it is easy to fall into several typical pitfalls. The following summarizes from five dimensions: **code quality, maintainability, security, requirement evolution, and…
This is a question most programmers face. **Vibe Coding** and **Spec Coding** are two distinct paradigms when programming with large language models (LLMs). The core difference is: **Is your input to the AI a vague feeling or a precise specification?** --- ## 1. A simple analogy: Cooking - **Vibe C…
Malicious query injection (malicious prompt injection / retrieval poisoning) is a very realistic security threat in the actual deployment of RAG systems. Attackers may use carefully crafted inputs to try to make the model leak sensitive information, bypass restrictions, execute unintended instructio…
Prompt tuning (Prompt Engineering / Optimization) is a key skill for making large language models "obedient." Especially in RAG systems, it directly determines whether the model faithfully adheres to retrieved content, avoids hallucinations, and follows output format specifications. --- ## 1. Core…
Optimizing RAG is not a single-step adjustment but a **full-chain optimization** process. Below I provide systematic optimization strategies from four dimensions: **data indexing side, retrieval side, generation side, and evaluation side**, along with practical experience that can be mentioned in in…
## What Exactly Does Embedding Do? — From Technical Essence to Interview Answers ### 1. Technical Essence: One Sentence to Capture the Core **The core task of embedding is to map discrete, unstructured data (text, images, etc.) into a continuous, low-dimensional vector space, so that semantically …
Accuracy is the core lifeline of a knowledge question answering system, especially when you try to apply it to serious scenarios (such as medical, legal, internal enterprise support). My view can be summarized as: **Accuracy is a multi-dimensional concept; you cannot just look at a single number, bu…
### What is RAG? **RAG** stands for **Retrieval-Augmented Generation**. Simply put, it is a technology that **"gives a large language model a reference book that can be consulted at any time"**. Imagine a large language model as a "super scholar" with an extraordinary memory and vast knowledge. B…
## 1. Core Concepts of Skill A Skill is a set of executable capability units encapsulated in an **Agent** or **AI system**. It typically includes: - **Trigger condition**: When it is called (e.g., user command, system event). - **Input parameters**: Data or context to be received. - **Execution lo…
## Three Core Methodologies of AI Agent: ReAct, Plan-and-Solve, and Reflection An AI Agent is an intelligent entity capable of autonomously perceiving its environment, making decisions, and executing actions. Its core methodologies mainly include three types: ReAct, Plan-and-Solve, and Reflection. …
## What is Mixture-of-Agents (MOA)? **MOA** is a **multi-agent collaboration architecture** whose core idea is to combine multiple independent AI models (called "experts" or "agents") through a **routing/scheduling mechanism**, allowing each expert to handle the subtasks it is best at, and finally …
# Agent Memory System Design: Implementation Solutions for Short-Term and Long-Term Memory This article explores the design of Agent memory systems, dividing them into two levels: short-term memory and long-term memory, and details their respective implementation solutions and considerations. ## F…
# Vector Database Interview Guide and Technical Analysis This article is an interview experience sharing and technical analysis of vector databases. It systematically explains the core concepts, technical principles, selection recommendations, and application scenarios of vector databases. ## 1. C…
# Function Calling Technical Summary ## 1. Definition Function Calling is a mechanism that allows developers to describe available external tools (such as APIs) to a large language model (LLM) via JSON schema. When the model determines that a tool is needed to answer the user's question, it outp…
# AI Interview Question 2: How to Ensure Reliable Tool Calling by Large Language Models (LLMs) How to ensure that large language models (LLMs) work reliably and controllably when calling tools, rather than relying solely on prompts to "convince" the model. A systematic multi-level constraint fram…
# Summary: Differences Between Agent Tool Calling and Ordinary Function Calling This article mainly discusses the core differences between Agent tool calling and ordinary function calling. It also elaborates in detail on the mechanism, value, common failure modes, and corresponding mitigation strat…