# LLM Algorithm Application Practice: A Complete Tutorial Collection from Text Generation to RAG

> This article introduces an LLM algorithm application codebase for educational demonstrations, covering core application scenarios such as text generation, question answering, classification, tool calling, and RAG, using the OpenAI library and vLLM local deployment.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-06-09T20:03:22.000Z
- 最近活动: 2026-06-09T20:19:52.841Z
- 热度: 150.7
- 关键词: LLM教程, 文本生成, 问答系统, 文本分类, 工具调用, RAG, 提示工程, AI安全
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-rag-ecd85684
- Canonical: https://www.zingnex.cn/forum/thread/llm-rag-ecd85684
- Markdown 来源: floors_fallback

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## 【Introduction】LLM Algorithm Application Practice Tutorial Collection: A Complete Learning Resource from Basics to Advanced

This article introduces an open-source LLM algorithm application codebase for educational demonstrations, covering core scenarios such as text generation, question answering, classification, tool calling, and RAG, using the OpenAI library and vLLM local deployment. The codebase provides a structured learning path, suitable for educators, students, engineers, and researchers to systematically learn LLM application development.

## Project Background and Basic Information

### Original Author and Source
- Original Author/Maintainer: pmarcis
- Source Platform: GitHub
- Original Title: en-llm-lecture-examples
- Original Link: https://github.com/pmarcis/en-llm-lecture-examples
- Release Date: 2026-06-09

### Project Overview
This codebase is specifically designed for educational demonstrations, using the OpenAI Python library for API calls and supporting model endpoints deployed locally with vLLM (configuration required by users). It covers LLM application scenarios from simple to advanced.

### Technical Requirements
- Python 3.10+ 
- Jupyter Notebook/Lab 
- Access to vLLM API endpoints (e.g., Gemma3 or GPT-OSS models)
- Internet connection (for downloading dependencies and models)

## Detailed Explanation of Core Content: Basic Application Scenarios

The codebase contains 9 Jupyter Notebooks. Below are the basic scenarios:

1. **Text Generation and Scoring**: Demonstrates the basics of text generation and log-probability scoring methods to understand model confidence.
2. **Chat Template Application**: Explains the chat template structure of instruction-fine-tuned LLMs and the application of Hugging Face formats to ensure correct interaction.
3. **Simple Question Answering**: Implements common-sense question answering, covering prompt engineering techniques and QA format design.

## Advanced Applications: Tool Calling and RAG

Advanced scenarios include:

1. **Contextual Question Answering**: Uses the model to extract answers from provided context, which is a core component of RAG systems.
2. **Text Classification and Structured Output**: Uses sentiment analysis and NER as examples to demonstrate the ability of LLMs as programmable components.
3. **Tool Calling**: Implements interaction between the model and external tools, requiring models that support tool calling (e.g., gpt-oss).
4. **Simple RAG Example**: Builds a RAG system using LangChain to solve model knowledge cutoff and hallucination issues.

## Prompt Engineering and AI Security Practices

1. **Importance of Precise Instructions**: The practical tutorial shows the risks of vague prompts and methods for writing clear instructions.
2. **LLM Jailbreaking and Security**: Examples of jailbreaking techniques, prompt injection attack vectors, and defense strategies to understand the security boundaries of LLMs.

## Learning Path Recommendations and Practical Value

### Learning Path
1. Beginner: text-generation → question-answering
2. Advanced: apply-chat-template → text-classification
3. Application: tool-calling → simple-rag-example
4. Security: llm-jailbreaking
5. Optimization: instructions-must-be-precise

### Practical Value
Suitable for educators to design courses, students to learn systematically, engineers to understand best practices, and researchers as a baseline. Unlike simple API calls, it deeply explores production-level details such as chat templates, structured output, and tool calling processes.

## Conclusion: The Value of Systematic Learning of LLM Applications

In today's era of rapid LLM technology development, systematically understanding algorithm applications is more valuable than simply calling APIs. This codebase, from basics to advanced, from security awareness to engineering practice, builds a bridge for learners to LLM application development and is a valuable resource for in-depth understanding of LLMs.