# curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain

LangChain & Prompt Engineering tutorials on Large Language Models (LLMs) such as ChatGPT with custom data. Jupyter notebooks on loading and indexing data, creating prompt templates, CSV agents, and using retrieval QA chains to query the custom data. Projects for using a private LLM (Llama 2) for chat with PDF files, tweets sentiment analysis.

Repository: https://github.com/curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain
Canonical: https://ross.abutalabs.com/products/get-things-done-with-prompt-engineering-and-langchain
Homepage: https://mlexpert.io
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: chatgpt, gpt-4, gpt4, prompt-engineering, artificial-intelligence, deep-learning, language-models, openai, python, langchain, large-language-models, llama2
Last push: 2024-01-07T15:10:58+00:00

## Health v2 (maintenance only)
Score: 30/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 88
- inputs: {"age_days": 1239, "days_push": 969, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1250, forks 372 (observed 2026-08-28T04:04:08.013493+00:00)

## What it is
A collection of Jupyter notebook tutorials on prompt engineering and LangChain for building LLM applications with ChatGPT/GPT-4 and open models like Llama 2. It covers loading and indexing custom data, prompt templates, chains, agents, chatbots with memory, fine-tuning, and projects like chatting with PDFs and sentiment analysis.

## Use cases
- learn langchain from scratch with tutorials
- build a chatbot that answers questions from my own pdf documents
- fine-tune llama 2 on a custom dataset
- create prompt templates and chains for llm apps
- run a private local llm chatbot with falcon or gpt4all
- do sentiment analysis of tweets with gpt
- understand retrieval qa chains with custom data

## When to choose
- you want hands-on notebook-based tutorials for LangChain and prompt engineering
- you need practical projects like chat-with-PDFs or local LLM chatbots
- you prefer free, self-paced learning with accompanying videos

## When to avoid
- you need a production-ready library or framework rather than educational notebooks
- you want up-to-date coverage of the latest LangChain APIs, as the material dates from 2023-2024
- you need structured courses with support and certificates rather than free tutorials

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: prompt-engineering, rag, agent-framework, chatbot, llm-training, nlp
- domain: large-language-models, artificial-intelligence, chatbots, tutorials
- platform: python, jvm-scripting
- tags: langchain, jupyter-notebooks, llama-2, chatgpt, openai, gpt-4, fine-tuning, qlora, vector-search, pdf-chat, retrieval-augmented-generation, natural-language-processing

## Member repositories
- curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain (main) score 30

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:08.013493+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 2026-08-30T05:07:44.179655+00:00, confidence not recorded.
  - readme: https://github.com/curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain (fetched 2026-08-28T04:04:08.013493+00:00, sha 45f36975e106)
  - homepage: https://mlexpert.io (fetched 2026-08-29T12:18:45.024632+00:00, sha 36849e3fc4f4)
  - site_page: https://www.mlexpert.io/academy/v1/setup-and-toolkit/local-ai-quickstart (fetched 2026-08-29T12:18:45.029444+00:00, sha 2b295caeb746)
  - site_page: https://www.mlexpert.io/academy/v1/setup-and-toolkit/langchain-quickstart (fetched 2026-08-29T12:18:45.031344+00:00, sha e7bd62e18b03)
  - site_page: https://www.mlexpert.io/changelog (fetched 2026-08-29T12:18:45.027496+00:00, sha 69be5f79923d)
- Data as of 2026-08-30T08:39:29.467469+00:00.