Large language models (LLMs) are at the forefront of today’s AI, not merely as technological marvels but as transformative agents reshaping how businesses innovate, operate and deliver value. Think of them as the wizards of words, capable of understanding language and transforming it in ways that benefit organizations.

However, as we stand on the cusp of this generative AI-driven revolution, it's imperative to explore not just the brilliance of LLMs in understanding and generating human-like text but also to set best practices for organizations to follow to increase business productivity, streamline operations, and unlock new realms of customer engagement.

For these purposes, LLMs alone are not enough. To truly benefit from these models, businesses must integrate them into a broader strategic framework akin to orchestrating a symphony. Each element, from data inputs to application interfaces, is crucial in achieving a harmonious outcome.

When thinking about how organizations can successfully implement LLMs into their workflow, three strategies come to mind.

1. Performance optimization: Reducing hallucinations and token cost

Using natural language processing (NLP) techniques in combination with LLMs helps to reduce hallucinations (the generation of incorrect or nonsensical information) and token cost (the computational resources required to process and generate language), improving the models’ output efficiency and accuracy. These strategies can enhance the overall performance and applicability of LLMs in various domains.

2. Governance: Steering the AI juggernaut

For organizations, responsibility is paramount. Picture your organization using LLMs for strategic market analysis. Here, governance steers these models toward ethical, fair, and responsible usage. Establishing policies around data privacy and eliminating biases become as important as maintaining compliance with regulatory standards. Governance also encompasses the rapid and continuous monitoring and updating of the model to keep it relevant and at peak performance.

3. Orchestration: The art of seamless integration

Orchestration in the context of LLMs refers to seamlessly integrating these models into existing business processes to turn the LLM’s output into an action. It's about creating an ecosystem where LLMs are integrated with other AI tools like enterprise resource planning (ERP) and decisioning systems. For instance, consider a scenario where a bank wishes to increase customer satisfaction by analyzing customer complaints. If the bank implements LLMs into its existing workflow, it can summarize and categorize complaints from different channels and generate a hyper-personalized email to the customer. This also takes into consideration sensitive customer information such as credit history.

LLM integration is complex but doable

While large language models are a transformative tool in the AI arsenal, their successful business deployment requires more than technical prowess. It demands a strategic blend of performance optimization, governance, and orchestration. Through performance optimization, you increase the accuracy of the LLMs while reducing the cost of usage. With governance as your shield, customer data remains secure and responses are unbiased. Orchestration transforms your support system into a well-organized structure, providing timely and efficient customer service while freeing up valuable human resources.

By approaching LLM integration as a complex, multi-faceted process, businesses can unlock new levels of efficiency, innovation, and competitive advantage, turning the promise of generative AI into a tangible business reality.

Want more? Read Generative AI and large language models demystified

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About Author

Marinela Profi

Global Marketing Manager - Data Science, Open source, AI

Marinela Profi is responsible for the integration of SAS Viya with open source technology and data science practice. Previously, Profi worked as an analytic and AI consultant. Over the past 5 years, Profi honed her skills mining data, developing models and technical/business solutions, including deploying AI at scale. She holds a Master’s in Statistics and a Master in Business Administration degree.

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