# PandaBearLab/prompt-tutorial

chatGPT、prompt、LLM

Repository: https://github.com/PandaBearLab/prompt-tutorial
Canonical: https://ross.abutalabs.com/products/prompt-tutorial
Homepage: https://ishell.online
License Family: other
Last push: 2024-06-13T10:47:33+00:00

## Health v2 (maintenance only)
Score: 25/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 63
- inputs: {"age_days": 889, "days_push": 811, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1326, forks 110 (observed 2026-08-28T04:04:22.805101+00:00)

## What it is
A Chinese-language tutorial series teaching prompt engineering for large language models like ChatGPT. It covers core principles such as clear instructions, structured output, few-shot prompting, and iterative prompt refinement, with practical examples for summarization, inference, translation, and chatbot building.

## Use cases
- learn how to write effective prompts for chatgpt
- prompt engineering tutorial for beginners
- how to get structured output from llms
- learn few-shot prompting techniques
- improve llm summarization and extraction results
- understand llm hallucinations and limitations
- prompt frameworks like CRISPE

## When to choose
- you are a non-technical reader wanting a gentle introduction to prompt engineering
- you prefer Chinese-language learning material with worked examples
- you want practical, task-based prompt recipes (summarize, translate, classify, generate emails)

## When to avoid
- you need advanced or research-level prompt engineering techniques
- you require an English-language resource
- you want a maintained software tool rather than a written course

## Facets
- artifact type: learning-resource
- maturity: active
- function: prompt-engineering, nlp, chatbot, documentation
- domain: large-language-models, tutorials, artificial-intelligence
- platform: cross-platform
- tags: prompt-engineering, chatgpt, llm, tutorial, chinese, course, natural-language-processing, web-server

## Member repositories
- PandaBearLab/prompt-tutorial (main) score 25

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:22.805101+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 2026-08-30T04:46:20.965913+00:00, confidence not recorded.
  - readme: https://github.com/PandaBearLab/prompt-tutorial (fetched 2026-08-28T04:04:22.805101+00:00, sha bfe5c6fe1e56)
  - homepage: https://ishell.online (fetched 2026-08-29T12:05:39.686030+00:00, sha 8762b4351af8)
  - site_page: https://ishell.online/about (fetched 2026-08-29T12:05:39.695753+00:00, sha c6aefe13964e)
- Data as of 2026-08-30T08:39:29.467469+00:00.