# phodal/aigc

《构筑大语言模型应用：应用开发与架构设计》一本关于 LLM 在真实世界应用的开源电子书，介绍了大语言模型的基础知识和应用，以及如何构建自己的模型。其中包括Prompt的编写、开发和管理，探索最好的大语言模型能带来什么，以及LLM应用开发的模式和架构设计。

Repository: https://github.com/phodal/aigc
Canonical: https://ross.abutalabs.com/products/aigc
Homepage: https://aigc.phodal.com/
Language: Rust
License Family: other
Topics: aigc, chatgpt, ebook, llm, opensource
Last push: 2024-01-23T10:02:45+00:00

## Health v2 (maintenance only)
Score: 19/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 83
- inputs: {"age_days": 1168, "days_push": 953, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1650, forks 186 (observed 2026-08-28T04:05:16.810883+00:00)

## What it is
An open-source ebook (in Chinese) on building real-world applications with large language models, covering prompt writing and management, LLM application architecture patterns like Unit Mesh, and fine-tuning with LLMOps. It aggregates the author's articles and related open-source projects exploring LLM-driven software development workflows.

## Use cases
- learn how to build applications with large language models
- learn prompt engineering patterns and best practices
- understand LLM application architecture design
- decide when to fine-tune an LLM for a specific domain
- learn how to integrate LLMs into software development workflows
- get started with LLMOps and LoRA fine-tuning

## When to choose
- you want a free, structured introduction to LLM application development and architecture
- you prefer Chinese-language learning material on prompt engineering and LLMOps
- you want curated links to hands-on LLM open-source projects and tutorials

## When to avoid
- you need an up-to-date reference, as the content was last updated in early 2024
- you need a runnable tool or library rather than a book
- you need English-language documentation

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: prompt-engineering, llm-training, documentation
- domain: large-language-models, tutorials, developer-tools, artificial-intelligence
- platform: rust
- tags: ebook, llm-application-architecture, prompt-engineering, llmops, fine-tuning, unit-mesh, chinese-language, web-server

## Member repositories
- phodal/aigc (main) score 19

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:16.810883+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:45:05.706972+00:00, confidence not recorded.
  - readme: https://github.com/phodal/aigc (fetched 2026-08-28T04:05:16.810883+00:00, sha edbc05251205)
  - homepage: https://aigc.phodal.com/ (fetched 2026-08-29T11:18:09.707402+00:00, sha a0a9c9e3d489)
  - site_page: https://aigc.phodal.com/misc/faq.html (fetched 2026-08-29T11:18:09.717875+00:00, sha 545f2ad91810)
- Data as of 2026-08-30T08:39:29.467469+00:00.