Understand the project
Practical overview
When it fits
Use it when you want notebook-based examples to study or adapt while learning how LangChain connects language models with prompts and custom data.
Who it is for
- Developers learning prompt engineering and LangChain through notebook examples.
- Readers exploring LLM workflows that load and query custom documents or CSV data.
Documented uses
- Learn to load and index custom data for retrieval-based question answering.
- Explore prompt templates and LangChain application patterns.
- Study examples of CSV question-answering agents, PDF chat with Llama 2 and tweet sentiment analysis.
Before you choose
Requirements & limits
The repository is public and specifies an Apache-2.0 license.
- The materials are tutorials and example projects rather than a hosted LLM application.
- The repository is identified as Jupyter Notebook-based and its examples use LangChain with named model options including ChatGPT/GPT-4 and Llama 2.
- Persistent indexing and substitution of another model provider are not demonstrated in the available project description.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Source-based practical guide
Open a notebook tutorial in Google Colab
Access the repository’s notebook collection through its Colab link.
Before you start
- A web browser with access to Google Colab.
Steps
Open the repository README.
Select the “Open In Colab” badge.
Browse the notebooks available through the linked repository collection.
Expected outcome
The repository’s notebook collection is opened through its Google Colab link.
Evidence and transparency
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 15 October 2026
- Original website title
- curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain · Official homepage title · retained in its source language