Revolutionizing Prompt Engineering: The Mass Production of ChatGPT Prompt with Langchain Library
Welcome to my channel, where we explore the latest advancements in prompt engineering and how they are revolutionizing the way we communicate. Today, we will be discussing the mass production of ChatGPT prompt with Langchain Library, a breakthrough technology that is changing the game for natural language processing.
The jupyter notebook can be found at
https://github.com/insightbuilder/python_de_learners_data/blob/main/code_script_notebooks/projects/chatgpt_nbs/example_langChain.ipynb
The sqlite database is found at
https://github.com/insightbuilder/python_de_learners_data/blob/main/code_script_notebooks/projects/chatgpt_nbs/chinook.db
Jupyter notebook has to be executed at https://colab.research.google.com/
As you may know, prompt engineering is the process of creating prompts that can be used to generate text in a specific style or format. This technology has been around for a while, but recent advancements in the Python Ecosystem and hard work of the programmers in Langchain team (https://github.com/hwchase17/langchain) have made it possible to create prompts that are composable and do lot more than just generating text.
One of the most exciting developments in this field is the mass production of ChatGPT prompt with Langchain Library. ChatGPT is a language model that was developed by OpenAI. Creating prompts for ChatGPT can be a time-consuming and labor-intensive process. Langchain makes the experience of Creating Prompts, a superb experience. Not just that, it allows to connect many other services like Search APIs, and external datasets and query them using natural language processing.
Langchain Library and its modules, classes and methods allows us to have a high-quality interaction with ChatGPT like LLMs. That too in a fraction of the time. Langchain Library uses Object Oriented Programming, Templating and provides awesome API to not just one LLM, but more than 28 different LLMs.
The benefits of Langchain Library go beyond just saving time and effort. By automating the interaction with LLM, we can also ensure that the outputs generated are high-quality. This is especially important in applications where accuracy and precision are critical, such as in legal or medical writing.
So, if you’re interested in learning more about the latest advancements in prompt engineering and natural language processing, be sure to subscribe to my channel. We’ll be exploring these topics in-depth and sharing the latest news and developments as they happen. Thanks for watching!
Related resources
Python download at https://www.python.org/downloads/
Learn to setup python: https://youtu.be/Csdg3V2k214
Git download at https://git-scm.com/
Learn to work with Git: https://youtu.be/0-KS_9x_oUM
Related playlists
Python Ecosystem of Libraries
https://www.youtube.com/playlist?list=PLbzjzOKeYPCoNAsZs679iXwsdP44G5SDS
ChatGPT and AI Playlist
https://www.youtube.com/playlist?list=PLbzjzOKeYPCpp3NCeQioevM0YpZa5VqcS
PS: Got a question or have a feedback on my content. Get in touch
By leaving a Comment in the video
@twitter Handle is @KQrios
@medium https://medium.com/@kamaljp/about
@github https://github.com/Kamalabot