1. **Analyze the Request:** * **Goal:** Expand the previous draft to 500-1000 Chinese characters (excluding punctuation and English). * **Method:** Add more details about learning sequence, prerequisites, post-learning output, and material usage. Remove redundancy and empty phrases. Do not exceed 1000 characters. * **Constraints:** Only output HTML snippet (`
`, `
`, `
`, `
`). No `/`. No file names (zip/mp4). No cliches. No AI self-identification. No other site names. No fabricated modules/tools. Simplified Chinese. 2-3 subheadings (but wait, the previous draft had 4 subheadings. The prompt says "分 2～3 个小标题" in the first instruction, but the second instruction says "不要重复堆砌标题". Let's stick to 2-3 main subheadings to be safe and concise, or 3 if it fits better. Let's do 3 main subheadings: 1. 解决的问题, 2. 适合基础与学习路径, 3. 学完产出与资料用法). * **Topic:** RAG与Agent性能调优50讲 (RAG and Agent Performance Tuning 50 Lectures). * **Directory clues to incorporate:** 01 (Unified multi-source formats: PDF, Word, images), 02 (Cross-page table alignment), 03 (Domain terminology word library), 04 (Sliding window + keyword semantic slicing), 05 (Dynamic overlapping slicing), 06 (OCR error correction), 07 (Text/image correction, layout detection), 08 (Configurable/extensible automated preprocessing pipeline), 09 (FAISS/HNSW/BM25 selection). 2. **Drafting - Section by Section (aiming for 500-1000 Chinese characters):** * *Subheading 1: 这门课解决什么问题* * Expand on the core engineering pain points of RAG/Agent: ingestion, processing, indexing, retrieval. * Draft: RAG与Agent系统上线后常遇召回率低、语义割裂、索引质量差等工程痛点。本课聚焦从文档解析、切片对齐、向量化索引到检索召回的全链路调优，解决多源异构数据接入、表格结构化、领域术语对齐等真实落地难题。 * *Subheading 2: 适合什么基础与建议先看哪几块* * Expand on prerequisites and learning path based on the 9 modules. * Draft: 适合具备Python基础及初步大模型应用开发经验的开发者。建议按工程链路顺序学习：先通读“多源文档统一解析”模块，掌握PDF、Word及图片的混杂内容处理；接着看“切片语义对齐”与“动态重叠切片”模块，解决固定切片召回率低的问题；随后关注“领域术语词库”与“OCR纠错”，提升检索一致性；最后进入“向量化索引选型”与“自动化预处理流水线”，建立整体工程视角。 * *Subheading 3: 学完能独立做什么与资料怎么配合练习* * Expand on post-learning output and material usage (source code, reusable modules). * Draft: 学完后可独立搭建并调优一套生产级RAG流水线。面对企业知识库，能自主完成文档解析、表格合并与切片策略配置；在检索阶段，能根据业务场景选择合适的向量索引结构，并通过动态切片与术语对齐等手段，显著提升检索一致性与召回率，让Agent输出更稳定可靠。 * Draft (Materials): 资料包以源码与工程配置为核心，提供可直接落地的模块化代码与流水线配置。建议按模块拆分，边看边改代码，重点打磨预处理流水线的可配置性与索引选型逻辑，通过修改切片参数与检索策略，直观对比性能指标变化，实现从调参到工程重构的闭环实践。 3. **Counting and Refining Characters:** * *Subheading 1:* 这门课解决什么问题 (9) RAG与Agent系统上线后常遇召回率低、语义割裂、索引质量差等工程痛点。本课聚焦从文档解析、切片对齐、向量化索引到检索召回的全链路调优，解决多源异构数据接入、表格结构化、领域术语对齐等真实落地难题。 (104) -> Total: 113 * *Subheading 2:* 适合什么基础与建议先看哪几块 (14) 适合具备Python基础及初步大模型应用开发经验的开发者。建议按工程链路顺序学习：先通读“多源文档统一解析”模块，掌握PDF、Word及图片的混杂内容处理；接着看“切片语义对齐”与“动态重叠切片”模块，解决固定切片召回率低的问题；随后关注“领域术语词库”与“OCR纠错”，提升检索一致性；最后进入“向量化索引选型”与“自动化预处理流水线”，建立整体工程视角。 (182) -> Total: 196 * *Subheading 3:* 学完能独立做什么与资料怎么配合练习 (18) 学完后可独立搭建并调优一套生产级RAG流水线。面对企业知识库，能自主完成文档解析、表格合并与切片
