# NirDiamant/Prompt_Engineering

22 prompt engineering techniques with hands-on Jupyter Notebook tutorials, from fundamental concepts to advanced strategies for leveraging LLMs.

Repository: https://github.com/NirDiamant/Prompt_Engineering
Canonical: https://ross.abutalabs.com/products/prompt_engineering
Homepage: https://diamant-ai.com
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Topics: ai, prompt-engineering, tutorials, llms, python, genai, llm, chatgpt, claude, langchain, openai, prompting, chain-of-thought, few-shot-learning, generative-ai, gpt, in-context-learning, machine-learning
Last push: 2026-08-19T19:37:08+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 35, longevity 49
- inputs: {"age_days": 692, "days_push": 14, "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 7818, forks 1018 (observed 2026-08-28T04:10:05.257015+00:00)

## What it is
A collection of 22 hands-on Jupyter Notebook tutorials covering prompt engineering techniques for large language models, from basic prompt templates to advanced strategies like chain-of-thought, self-consistency, and tree-of-thought prompting. It serves as an educational resource for learning and implementing prompting methods with code examples.

## Use cases
- learn prompt engineering from scratch
- understand chain-of-thought prompting with code examples
- compare few-shot vs zero-shot prompting techniques
- implement tree-of-thought and self-consistency prompting
- find practical LLM prompting patterns for building applications
- study in-context learning strategies
- get hands-on notebooks for ChatGPT and Claude prompting

## When to choose
- you want hands-on, runnable notebooks to learn prompting techniques
- you need a structured progression from basic to advanced prompt engineering
- you prefer code-first tutorials over theory-only articles
- you want coverage of many techniques (22) in one repository

## When to avoid
- you need a production prompt management or evaluation tool rather than tutorials
- you want a library or SDK to integrate into your codebase
- you need non-Python examples or a non-notebook learning format
- you require guaranteed licensing terms for commercial reuse (license is non-standard)

## Facets
- artifact type: learning-resource
- maturity: active
- function: prompt-engineering, llm-inference, nlp
- domain: large-language-models, tutorials, artificial-intelligence
- platform: python, cross-platform
- tags: jupyter-notebooks, chain-of-thought, few-shot-learning, genai, hands-on-tutorials, openai, langchain, natural-language-processing

## Member repositories
- NirDiamant/Prompt_Engineering (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:05.257015+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 2026-08-29T17:34:09.840361+00:00, confidence not recorded.
  - readme: https://github.com/NirDiamant/Prompt_Engineering (fetched 2026-08-28T04:10:05.257015+00:00, sha 3ead92f89d2f)
  - homepage: https://diamant-ai.com (fetched 2026-08-29T08:30:39.204783+00:00, sha b674eb24f089)
- Data as of 2026-08-30T08:39:29.467469+00:00.