Navigating LLM-powered app development in 2024

With its rapid growth taking off in late 2022, the field of AI has been flourishing ever since. And the disruption is definitely far from slowing down: 75% of companies expect artificial intelligence to yield significant or disruptive change in their respective industries in years ahead according to McKinsey.

Being a huge productivity driver, Gen AI stands with a massive potential to bring a few trillions of dollars in value to the global economy annually by 2040.

And adoption is clearly on rise: recent industry data indicates a significant surge in AI adoption by respondents' organizations to a whopping 72% with a growing interest in gen AI – up from around 50% over the past six years. 

In reality though, adopting GenAI is not a one-size-fits-all process. It necessities a tailored approach, taking into account the specific needs and circumstances of each organization to maximize AI utility and drive competitive advantage.

In this article, let’s explore what it takes to adopt GenAI, what approach stands efficient and what are the potential benefits to reap.

Exploring the Value of Customizing GenAI

Recently, McKinsey is observing a growing trend towards customizing AI to create tailored solutions that meet specific business needs and maximize value.

Such an approach poses multiple benefits, among which are:

1. Development cycle shortcut or greater cost-efficiency

Starting development from scratch may be too resource-intensive. In contrast, using a customization approach, you can significantly optimize the development cycle, thereby saving time and resources and reducing time to market. Flexible deployment scenario.

2. Enhanced functionality 

With the customization approach you can make the most out of your AI solution and capitalize on the opportunity of developing additional features to extend its functionality and drive greater value for your business.

3. Adaptability and security

Another advantage of the customization approach is the flexibility it offers to implement the solution in a way that best suits your operational needs and security requirements. Whether it’s in the cloud or on-premise, the AI solution can be tailored to integrate seamlessly with your existing infrastructure, ensuring optimal performance and compliance.

Steering Towards Streamlined LLM-powered App Development

Here's a brief overview of the essential steps and considerations needed for a successful AI customization.

Step.1 Define business need 

Start your journey by identifying the specific business challenges, goals and objectives that can be tackled and accomplished through AI. Keep in mind that implementing AI just for the sake of it is a recipe for disaster. Identifying the areas where an AI solution can truly provide a competitive edge is essential.

Step 2. Outline potential use cases 

As soon as the business needs are identified, you are ready to chart a portfolio of use cases called to address those needs.

This involves examining a range of  AI applications, such as automating routine tasks, enhancing customer service, or utilizing predictive analytics to facilitate decision-making processes. Then these use cases should be prioritized based on their potential impact and feasibility.

Consider that Generative AI can facilitate the automation of complex use cases. AI agents, powered by foundation models, can adjust on the fly, handling intricate, unpredictable workflows that traditional rule-based systems find challenging.

Step 3. Combine AI components

Moving forward, the next step is to decide on the components crucial for any AI solution. 

Start with the selection of an appropriate AI model that matches your operational requirements and desired performance levels. Additionally, consider the benefits of utilizing multimodal capabilities to boost performance and more effectively cater to a wide range of user requirements.

The next crucial component is the vector database designed to store and manage embeddings – numerical representations that convert data into a format that can be processed by LLMs, allowing them to capture the semantics of the text and thereby enabling efficient processing and retrieval.

Another component worth mentioning is memory and state management, which is essential to ensure that AI can learn from iterations, improve over time, and offer personalized responses by retaining context over multiple interactions.

Among other components stand APIs and Integration Tools that enable the integration of LLM capabilities into various applications. By enabling seamless interaction and real-time data exchange between enterprise systems, APIs and integration tools elements contribute to making AI more accessible, scalable, and feature-rich.

Step 4. Choose a data pipeline approach

Foundation models, while being extensively pre-trained on large datasets, neither possess real-time data nor directly correspond to your specific business information. For that reason, it’s critical to augment their knowledge capacity with extra data to bridge this gap.

Given today’s advancements and rapid pace of the market, a fine-tuning approach may stand resource-intensive. Instead, employing a RAG approach (with advanced scenarios included), and making use of API function calls allows your solution to remain updated with your existing corporate data and agile in incorporating the most up-to-date information from web pages or enterprise systems.

Keep in mind that AI solutions are highly dependent on data. Thus, high-quality, representative data is essential for optimal model performance.

Step 5. Settle on a deployment scenario 

When it comes to AI deployment, there are several options you can explore depending on your specific needs. These include deploying your AI systems in the cloud (AIaaS), opting for an on-premise environment, or utilizing a hybrid approach that combines the benefits of both. Each method offers unique advantages in terms of scalability, control, and flexibility, allowing you to choose the one that aligns best with your organization's infrastructure and goals.

Final thoughts

Everybody is talking about AI; all conferences feature AI banners, and every company booth is adorned with marketing slogans about it. With AI being all the rage, now is the right time to move beyond mere discussions and start integrating AI into the company's daily work routines. Because AI isn't just a trend; it's a significant shift in how businesses operate.

Some forward-thinking companies have already begun this transition, harnessing the power of AI to streamline processes, enhance productivity, and drive innovation. And as AI technology continues to evolve, the pace of adoption will only intensify exponentially. Therefore, there's no time to waste for companies looking to stay ahead and preserve a competitive edge in the market. With AI agents being the new AI frontier of Gen AI, the time to act towards practical AI adoption is now.