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From Labs to Market: The Journey of Productizing Large Language Model Systems

Anantha Sharma

Head of AI Architecture & Strategy , Charlotte, US

Prag Jaodekar

Technology Director - CTO Office , London, UK

Artificial Intelligence

Large language models (LLMs) are groundbreaking tools with the potential to revolutionize various industries. However, translating cutting-edge research into real-world applications, otherwise known as "productization," is a challenging and multifaceted process.

Let’s break down this complex journey and identify the key considerations involved in taking an LLM system from the research lab’s-controlled settings to the hustle and bustle of the commercial marketplace.

Phase 1: Conception and Development

The process begins with excitement and potential within your organization about the possibilities of GenAI technology. The initial step is crystal clear: Define the exact problem the system will aim to address. Here’s how to do it:

  1. Identify the use case

    The first order of business is finding a real problem that the LLM can tackle. This makes sure the technology meets a genuine need and resonates with future users. A great way to refine these ideas is by involving various business units in a design thinking workshop, exploring the “art of the possible.” Early engagement with the business side not only helps in honing the idea but also in securing ongoing support and finding, setting the stage for a smooth rollout once the solution goes live.

  2. Choosing the right architecture

    Now, you need to select an appropriate LLM architecture. Various architectures have their own set of strengths and are suited to different tasks. You’ll need to consider factors like performance goals, training needs, and resource availability to make the best choice.

  3. Data acquisition and curation

    Data is the lifeblood of any LLM system. This phase emphasizes collecting data that’s relevant and high-quality. It’s also vital to acknowledge and address potential biases in the data to ensure the LLM system's fairness and ethical operation. Additionally, ensuring compliance with relevant ethical and legal regulations is paramount throughout the data acquisition process.

  4. Training and fine-tuning

    With the data prepared, the next step is training your LLM architecture on this dataset. This may include fine-tuning the model to better suit the specific problem. Monitoring the training process closely is key to identifying and correcting any biases or issues, ensuring the LLM is effective and developed responsibly.

Phase 2: Engineering for Production

After laying the groundwork, the focus shifts refining the LLM for practical application and ensuring it communicates effectively with users.

Model optimization is key, as LLMs tend to be resource-intensive. By applying techniques like model compression and quantization, the goal is to boost efficiency and reduce latency without compromising on the model’s accuracy. This step ensures the LLM can deliver top-notch performance even on a leaner resource budget.

Transitioning from the internals of the LLM to its interface with the outside world, the development of an intuitive API becomes the next focal point. This interface must not only be secure and scalable but also designed with the end-user in mind, enabling straightforward access to the LLM’s features.

As the LLM begins to take shape, attention shifts to the infrastructure that will support its deployment. Cloud platforms offer scalability and flexibility, while on-premise deployments provide greater control and security. The specific choice depends on factors like the LLM's resource requirements, desired level of control, and security considerations.

Finally, implementing comprehensive monitoring and logging systems is essential. These systems track the LLM's performance, identify potential issues like performance degradation or data breaches, and ensure regulatory compliance.

Phase 3: Deployment and Launch

With a robust LLM system engineered for production, it's time to introduce it to the world. The initial step of this phase involves a careful rollout through A/B testing. This method allows teams to gradually introduce a limited user base, creating an opportunity to gather insights and pinpoint any unforeseen issues before a full-scale launch.

Then, this process evolves into a cycle of feedback and refinement. The emphasis on continuously collecting user feedback post-launch transforms the LLM into a living system that grows and adapts over time. This iterative process of incorporating user feedback ensures the LLM system remains relevant and effective over time.

Supporting this user-centric approach, the provision of thorough documentation and support resources becomes paramount. Equipping users with the knowledge and tools to effectively utilize the LLM system ensures a smoother experience, encouraging broader adoption and deeper integration into users’ workflows.

Key Considerations Throughout the Journey

Embracing LLMs offers exciting prospects for business innovation, yet it carries significant responsibilities.

Ensuring these systems are transparent in their decision-making is crucial for trust and usability. Equally important is maintaining stringent security and privacy measures to protect user information. Ethical deployment is non-negotiable, demanding continuous efforts to eliminate biases and ensure fairness. Moreover, staying up to date with the evolving regulatory landscape is very important for the business as the cost of retrospective compliance is quite high.

In summary, the deployment of LLMs must be approached with a commitment to clarity, safety, ethical, and adherence to legal standards. That way, we can ensure technology serves us well and responsibly.

The Author

Rachel Anderson, Digital Lead at Synechron UK
Anantha Sharma

Head of AI Architecture & Strategy

Anantha Sharma is a Senior Director and the Head of AI Architecture & Strategy. In this role he combines domain knowledge, mathematical skills, and expert architectural & programming skills to extract robust, repeatable, and meaningful insights from data.

To find out more, email him at:

Prag Jaodekar is Technology Director at CTO Office of Synechron, based in the UK. He supports Synechron’s business units and clients with strategy, architecture and engineering expertise that spans Synechron’s business and technology capabilities. Prag has more than 18 years of experience as a consultant of technology and application architect specialist creating IT strategies and delivering solutions for many top tier banks across the financial services industry.

To learn more about Synechron’s opinions, skills and abilities on any of these mainstay and emerging technologies, or to learn how we can advise your company on ways to deploy these for business optimization purposes, please reach out to:

Rachel Anderson, Digital Lead at Synechron UK
Prag Jaodekar

Technology Director - CTO Office

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