Unlocking Productivity: Custom AI Models for Code Generation

Context

We aim to address the limitations of existing AI powered code generation solutions, such as accuracy for specific programming frameworks, privacy concerns and cost. By leveraging our expertise and embracing the potential of Custom AI, we adopt a white glove process comprising five core steps. This case study illustrates our systematic approach leading to enhanced software development productivity, improved in-house capabilities, and expanded business opportunities.

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Our approach

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Step 1: Scope

The initial phase involved enumerating various use cases and prioritizing them based on their significance. The most prominent use case identified was to create a private code completion IDE plugin for large and complex codebase to adapt an existing and specific in-house programming code-style.

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Step 2: Prototype

We decided to concentrate on one specific use case: code completion for one particular programming framework. To measure the success of the custom AI model, we defined key business metrics with the client like team velocity and test coverage. The company collected relevant training data (screenshot) to train their custom AI model. Additionally, we conducted benchmark tests to validate the potential improvement over existing solutions. A series of A/B tests were performed to compare the performance of the custom AI model with other existing approaches, ensuring its viability and efficacy.

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Step 3: Deployment

Upon achieving positive results during the prototype phase, we moved forward to deploy the custom AI model into production for the initial use case. Collaborating with our client, we identified an appropriate Operations Infrastructure (Ops Infra) that aligned with their requirements. The model was deployed for a specific internal software engineering team with huge feature backlog. Backtesting was conducted to validate the impact of the custom AI model and measure the resulting increase in the team velocity.

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Step 4: Improvement

With the model deployed in production, the company proactively monitored its performance in the implemented use case. We analyzed the gathered data, user feedback, and other relevant metrics to identify areas of improvement. Simultaneously, we expanded the application to other code generations use cases, such as creating unit testing to increase the test coverage and reduce regressions. The process for these additional use cases followed the same steps as in the prototype and deployment phases, ensuring consistency and replicability.

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Step 5: Training and Team Empowerment

We provided necessary training to bring everyone up to speed on the new tools to understand the new capabilities they offer as well as their limitations and how they could improve them. We also helped software engineer managers and product managers adapt their processes given the new tools.

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Outcome

The implementation of the custom AI model resulted in a significant improvement in the company productivity. By addressing the limitations of existing solutions and developing tailored AI solutions, the client was satisfied as well as the teams using the tools. Moreover, the project led to an enhancement of in-house capabilities, with team members being able to focus on other high value added tasks. 

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Through a meticulous white glove process comprising five core steps, we successfully developed and deployed a custom AI model for customer support. By prioritizing use cases, prototyping, deploying, improving, and empowering our client’s team members, the company enhanced customer satisfaction and improved internal capabilities. This case study exemplifies the value of custom AI in addressing specific business needs and illustrates the potential for companies to leverage AI technology to gain a competitive advantage in their respective industries.

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