3 Ways Developers Can Leverage LLMs Right Now

More than ever, developers must stay ahead of the curve to keep workflows streamlined and software development on pace with business goals. LLMs can help.

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More than ever, developers must stay ahead of the curve—or at least at the curve—to keep workflows streamlined and software creation (and iteration) on pace with business goals. Enter LLMs (large language models), which are now must-have tools for most software development teams. Read on to understand how LLMs can fit into your development processes.

Use LLMs to Generate Functional Code

While the past 18 months have obviously seen unyielding excitement (and yes, some trepidation) around generative AI, many software developers and businesses remain unsure of its practical applications. More recent advances, particularly in code-generation LLMs like AppCoder, are changing that perception. Now, developers can give natural language prompts, and voila! The LLM churns out fully functional code. Functional-code-generating LLMs are a game-changer, promising a major productivity leap for teams diving into this tech.

We’re already starting to see developers—long accustomed to the tedious grind of traditional coding techniques as part of their job—leverage code-generating LLMs to automate repetitive tasks and not look back. These LLMs can reliably (and reliability is key) churn out the heavy lifting, freeing up developers to focus on the more exciting and strategically valuable work they’d rather be doing anyway. The buck doesn’t stop at mere automation, though. Developers will undoubtedly harness these LLMs to fuel faster innovation. Teams supported by capable code-generating LLMs will be able to iterate on their products and enhance digital experiences at an accelerated pace, releasing features and updates with a speed and agility that traditional, hand-coded development cannot replicate.

The machine-learning backbone of these code-generating LLMs also means that the benefits will only amplify over time. There is a significant opportunity for developers, right now, to begin gaining competitive advantages (or at least avoid falling behind) by adopting LLMs to help spin out functional code.

Use LLMs to Enhance Low-Code

Code-generating LLMs play a significant complementary role in advancing low-code development strategies and workflows. Low-code tools streamline development by simplifying code interactions. Now, with the advent of code-generation LLMs, development friction is reduced even more. The combination of low-code platforms and code-generation LLMs dramatically removes tedious tasks from developers’ workloads, leading to significant improvements in both productivity and, well, job satisfaction.

LLMs also contribute to reducing complexity in low-code development by automating more complex coding tasks, making it easier for developers with varying levels of expertise to build applications efficiently. This accessibility further democratizes software development and expands the pool of individuals who can contribute to building applications.

Use LLMs Privately

While public APIs will offer access to code-generation LLMs for everyone, leveraging these shared models can make it challenging to differentiate your applications and features. Public LLMs are trained on data from myriad organizations, inevitably leading to a certain homogenization in their outputs.

This is where private code-generation LLMs play a crucial role. By investing in training their own models on proprietary data, teams can create intellectual property that sets them apart. Developers using private LLMs can then build applications with unique capabilities and experiences unavailable to competitors who are using public APIs. As generative AI matures and pre-trained LLMs become commonplace, this ability to differentiate through private data will become increasingly valuable.

Shomron Jacob

Shomron Jacob is the Head of Applied Machine Learning and Platform at Iterate.ai, whose AI innovation ecosystem enables enterprises to build production-ready applications. Shomron began his career as a software engineer but soon found himself learning ML/AI, and switched his professional direction to follow it. He lives in Silicon Valley.


Shomron Jacob is the Head of Applied Machine Learning and Platform at Iterate.ai, whose AI innovation ecosystem enables enterprises to build production-ready applications. Shomron began his career as a software engineer but soon found himself learning ML/AI, and switched his professional direction to follow it. He lives in Silicon Valley.