curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain resource
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. observed · 2026-08-28
Health v2 · maintenance only
30/100
- Activity 0
- Release rhythm 35
- Longevity 88
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 1239
- days_rel: n/a
- days_push: 969
- n_releases_24m: 0
Adoption not part of the score
1250 stars · 372 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
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
learning-resource · maturity maintenance
prompt-engineering rag agent-framework chatbot llm-training nlp large-language-models artificial-intelligence chatbots tutorials python jvm-scripting langchain jupyter-notebooks llama-2 chatgpt openai gpt-4 fine-tuning qlora vector-search pdf-chat retrieval-augmented-generation natural-language-processing
5 sources
- readme: https://github.com/curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain · fetched 2026-08-28 · 45f36975e106
- homepage: https://mlexpert.io · fetched 2026-08-29 · 36849e3fc4f4
- site_page: https://www.mlexpert.io/academy/v1/setup-and-toolkit/local-ai-quickstart · fetched 2026-08-29 · 2b295caeb746
- site_page: https://www.mlexpert.io/academy/v1/setup-and-toolkit/langchain-quickstart · fetched 2026-08-29 · e7bd62e18b03
- site_page: https://www.mlexpert.io/changelog · fetched 2026-08-29 · 69be5f79923d
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain | main | 30 |
For agents
markdown · JSON · MCP: product_card(name="curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem