Using GenAI in software development

TL;DR: Generative AI tools like GitHub Copilot and GitLab Duo help you write code faster and with fewer errors. They also make it easier to improve the quality and consistency of your code.  

These assistants are the very entry-level of GenAI for software development. They help you write code. They are available now, and you should start using them right away. They offer several benefits. 

They can boost your productivity and code quality. They use vast code repositories to give you relevant suggestions. You can avoid errors, write cleaner code and reduce technical debt. It also makes it easier for your team to work together and onboard new members. 

GitHub Copilot and GitLab Duo are two popular examples of tools that help you deliver software faster:  

• GitHub Copilot suggests code, creates documentation and even writes tests.  

• GitLab Duo integrates with your CI/CD pipeline. It gives you feedback to improve your code in real time. 

A quick word about LLMs

Generative AI is changing how you create software. But how you use it depends on what you want to achieve. As we have covered previously, you can use it to improve your development process. But you can also use it to add new features to your software. 

When you use LLMs in your development process, they help your team work better. For example, you can use tools like GitHub Copilot or ChatGPT to generate code, fix bugs, or learn new frameworks. You can also train your own model on your codebase. This can help you automate tasks and focus on more complex problems. The LLM helps you write better code, faster. But it's not part of the final product. 

TL;DR: You can save money on GenAI by using a mix of third-party tools and your own infrastructure. Start with third-party tools and then move to your own infrastructure when you're ready.  

Using GenAI can be expensive if you're not careful. You can end up spending a lot of money on third-party services. These services often charge you for each token you use and the amount of data processed when using the models. This can limit your flexibility and what you can do with the technology.

A better approach is to use a hybrid strategy. Start with third-party platforms to get started quickly. These platforms give you the speed and flexibility you need in the early stages. Once you have validated your models, you can move them to your own infrastructure.  

While this move can sound complex, it gives you more control and can save you money in the long run. You can also use your existing hardware and software. 

TL;DR: Generative AI can work with more than just text. You can use it for audio and video too. Try using large vision models (LVMs) for tasks like monitoring. To use these models in your products, you'll need to integrate them with edge cloud and use powerful GPUs.

Generative AI is not limited to text. You can use it with audio and video as well. This allows you to create new and innovative products and services. 

You can start by using large vision models (LVMs). These models can analyse images and videos. You can use them for tasks like monitoring and alerting. But keep in mind that LVMs don't have a good long-term memory. So, focus on use cases that need short-term analysis. 

To use LVMs in your products, you should consider integrating them with edge cloud. This allows you to process data closer to the source. You also need to invest in powerful GPUs to handle the workload.  Combining edge cloud and LVMs can lead to new and exciting applications. 

TL;DR: Use templates and access controls to manage your GenAI assistants effectively. Create a central library of assistants that everyone in your organisation can use. It can help you avoid redundant work and ensure everyone is using the same standards.    

Using GenAI assistants can be easy for individual tasks. But it can be harder to use them across your whole organisation. You need a structured approach to manage them. 

You should standardize your assistants. You can use templates to do this. It makes it easier to deploy and customize them. You should also use role-based access control (RBAC) so you can control who can create and modify assistants. 

You can also create a central library of assistants so your team can share and reuse assistants. It also helps you avoid duplication and improve efficiency. 

There are types of assistants: 

1. Local assistants, for specific tasks 
2. Common assistants, for general use across your organisation 

Use a centralized system to manage your assistants. It helps you track their lifecycle and keep them up to date. 

TL;DR: GenAI can help you modernize your old systems. It can automate tasks like creating documentation, updating databases and refactoring code. This can save you time and money while making your systems more reliable.    

You need to keep your IT systems up to date. But this can be difficult if you have old software and poor documentation. GenAI can help you with this. It can automate tasks like creating documentation and analyzing your systems, saving you time and money. 

GenAI can also help you improve your code. It can find problems and suggest improvements. It can even translate your old code into newer languages. 

This helps you keep your code up-to-date and secure. For example, you can use GenAI to update old parts of your code with more modern and efficient approaches. Your code becomes easier to work with and less likely to have problems. 

GenAI can help you overcome the challenges of modernizing your IT systems. It can help you create better documentation, improve your code and make your systems more reliable. 

TL;DR: Validate the inputs and outputs of your AI agents, making sure these are working correctly and following the rules.     

You need to make sure your AI agents are working correctly. You can do this by validating their inputs and outputs. In other words: checking the data they receive and the results they produce. 

You can use frameworks like LLamaIndex and RAG to help you with this. They allow you to build the evaluation engine which is interacting with your systems to validate the responses and outputs. 

Integrating validators into your development process can be challenging. You need to ensure your agents meet functional and non-functional requirements. Functional requirements are things like test cases. Non-functional requirements are things like performance and security.

How to get started developing validators   

1. Connect your agents to your regression suite for functional specifications
2. Connect your CI/CD workflows to your product and service documentation for both non-functional and functional requirements
3. Integrate with your ITSM systems to validate user-related processes 
4. Incorporate your CRM systems to verify customer interactions 

Validating your AI agents helps you reduce risks and ensure they are working as expected. 

TL;DR: You need a secure environment to run your AI agents. You also need to control who has access to them. This helps you protect your data and your systems.     

You need a special environment to run your AI agents. This environment gives your agents the resources they need, like computing power, memory and access to data sources.  

It also includes tools to monitor your agents and make sure they are running smoothly. These tools might include performance monitoring, logging and alerting capabilities, helping you spot any bottlenecks that come up. 

You also need to control who has access to your agents. You can do this with role-based access control (RBAC). This allows you to give different people different levels of access, based on their roles and responsibilities. For example, you might give developers access to modify agents, but only allow administrators to deploy them to production.  

You can also use RBAC to restrict access to sensitive data and workflows, so that only authorized personnel can interact with them.