llm-app-patternslisted
Install: claude install-skill LiHongwei-cn/lihongwei-cn
# 🤖 LLM Application Patterns
> Production-ready patterns for building LLM applications, inspired by [Dify](https://github.com/langgenius/dify) and industry best practices.
## When to Use This Skill
Use this skill when:
- Designing LLM-powered applications
- Implementing RAG (Retrieval-Augmented Generation)
- Building AI agents with tools
- Setting up LLMOps monitoring
- Choosing between agent architectures
---
## 1. RAG Pipeline Architecture
### Overview
RAG (Retrieval-Augmented Generation) grounds LLM responses in your data.
```
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Ingest │────▶│ Retrieve │────▶│ Generate │
│ Documents │ │ Context │ │ Response │
└─────────────┘ └─────────────┘ └─────────────┘
│ │ │
▼ ▼ ▼
┌─────────┐ ┌───────────┐ ┌───────────┐
│ Chunking│ │ Vector │ │ LLM │
│Embedding│ │ Search │ │ + Context│
└─────────┘ └───────────┘ └───────────┘
```
### 1.1 Document Ingestion
```python
# Chunking strategies
class ChunkingStrategy:
# Fixed-size chunks (simple but may break context)
FIXED_SIZE = "fixed_size" # e.g., 512 tokens
# Semantic chunking (preserves meaning)
SEMANTIC = "semantic" # Split on paragraphs/sections
# Recursive splitting (tries multiple separators)
RECURSIVE = "recursive" # ["\n\n", "\n", " ", ""]
# Do