Generative AI: How Software and AI Engineers Differ in their approach

Nowadays, software engineers are exploring the world of artificial intelligence (AI) and developing large language model (LLM) products using various APIs and frameworks. While this approach appears promising, there are many important aspects that are often overlooked.

The Software Engineering Mindset

Software engineers are adept at designing and consuming APIs. So they find it very easy to use these Pre Trained Gen AI API and demoing the solution but they miss the fundamental approach . AI engineering and its related solution requires understanding and working with data. For example , when we are working on building a Gen AI powered chat assistant , its important to understand the data which will be feed in . Depending on the data , choosing the right embedding technique is important as different embedding technique generate different results .

Another key aspect often overlooked is selecting the right model with the right specifications. Not every solution requires a high-parameter model like GPT-4 or Gemini 1.5. These powerful models come with significant costs, so it's important to consider the return on investment. Often, a lower-parameter, open-source model can achieve the desired outcomes at a fraction of the cost.

The AI Engineering Mindset

AI engineering is all about understanding and working with data. Unlike software engineering, where pre-packaged APIs can be easily integrated, AI solutions require a deep dive into the specifics of the data you are working with. There are no one-size-fits-all APIs readily available for AI solutions. Instead, AI engineers must figure out how to convert input data into meaningful output through models or multiple models.

This process is inherently scientific. It involves:

• Choosing the right models and hyperparameters: With a vast array of models and settings to choose from, selecting the optimal combination is crucial.
• Labeling data: Accurate data labeling is essential for training effective models.
• Iterative testing, validation, and refinement: Achieving product-grade performance requires continuous experimentation and improvement.

Conclusion

The intersection of software engineering and AI presents a unique set of challenges and opportunities. For software engineers stepping into the AI world, embracing the data-centric, iterative, and scientific nature of AI development is essential. By understanding and adopting the AI engineering mindset, you can move beyond creating impressive demos and towards building robust, scalable AI products ready for production.

#AI #MachineLearning #LLMs #SoftwareEngineering #TechInnovation