How I Built Our Internal /askplivo AI-Powered Chatbot for Slack
Posted: Wed Dec 04, 2024 3:37 am
SMS
How I Built Our Internal /askplivo AI-Powered Chatbot for Slack
Plivo’s CTO Michael Ricordeau writes …
I’ve been exploring AI and wanted to test a few things on my own because that’s what I enjoy doing as CTO at Plivo. I wanted to learn about OpenAI tools and build something useful for the company chinese overseas europe data at the same time. The result was our new /askplivo chatbot for Slack.
We have a lot of resources on our website, including API documentation, blog posts, and use case guides. It can be challenging for our employees to navigate through them and find what they want.
Occasionally, I would see questions popping up on our Slack channels about a feature, or telecom regulations (I’m looking at you, 10DLC), or country coverage. If we could have a Q&A chatbot assistant capable of answering questions instead of waiting (desperately, sometimes) for a co-worker to reply we could be more productive.
For instance, imagine you’re a Plivo support engineer and a customer is asking whether Plivo supports SMS in France. (I use France as an example because that’s where I’m from, but you can use any other country; we support more than 190.) With a chatbot, you could get an answer in less than a minute.
Because my goal was to learn more about AI and OpenAI, I started exploring how I could build a chatbot that used Plivo’s content enhanced with the power of AI. I wanted something that would be smart enough to answer with relevant information and not hallucinate, which is a well-known problem.
Designing the AI chatbot
I like Python for prototyping and MVP because the syntax is simple, and I’ve been using it for 20 years. Granted, it’s not super performant, but I’m not planning to build the next Google here. I wanted a lightweight web framework, so I used Flask to expose the API to interact with Slack and RQ to process jobs in the background.
How I Built Our Internal /askplivo AI-Powered Chatbot for Slack
Plivo’s CTO Michael Ricordeau writes …
I’ve been exploring AI and wanted to test a few things on my own because that’s what I enjoy doing as CTO at Plivo. I wanted to learn about OpenAI tools and build something useful for the company chinese overseas europe data at the same time. The result was our new /askplivo chatbot for Slack.
We have a lot of resources on our website, including API documentation, blog posts, and use case guides. It can be challenging for our employees to navigate through them and find what they want.
Occasionally, I would see questions popping up on our Slack channels about a feature, or telecom regulations (I’m looking at you, 10DLC), or country coverage. If we could have a Q&A chatbot assistant capable of answering questions instead of waiting (desperately, sometimes) for a co-worker to reply we could be more productive.
For instance, imagine you’re a Plivo support engineer and a customer is asking whether Plivo supports SMS in France. (I use France as an example because that’s where I’m from, but you can use any other country; we support more than 190.) With a chatbot, you could get an answer in less than a minute.
Because my goal was to learn more about AI and OpenAI, I started exploring how I could build a chatbot that used Plivo’s content enhanced with the power of AI. I wanted something that would be smart enough to answer with relevant information and not hallucinate, which is a well-known problem.
Designing the AI chatbot
I like Python for prototyping and MVP because the syntax is simple, and I’ve been using it for 20 years. Granted, it’s not super performant, but I’m not planning to build the next Google here. I wanted a lightweight web framework, so I used Flask to expose the API to interact with Slack and RQ to process jobs in the background.