How consultants can help their clients implement LLMs

03 June 2024 4 min. read

Large Language Models (LLMs) are helping shape the way businesses are operating and making decisions. Consultants play a big role to play in the adoption of LLM – Greg Taylor from Databricks outlines how consultants can help their clients gain a competitive advantage through the use of LLM.

Many business leaders have jumped on the bandwagon since ChatGPT launched to the public, and many companies are now building their own models. In fact, the latest State of Data + AI report from Databricks found the interest in LLMs has surged by 1,310% over the past six months.

Consultants have a big role to play in this transformation. Consultants bring specialised knowledge and expertise in LLM advisory and implementation to the table which allows them to support business leaders.

How consultants can help their clients implement LLMs

Greg Taylor is Vice President for Consulting and Systems Integrator Partners for Asia Pacific and Japan at Databricks

Four ways consulting firms can help business leaders best implement LLMs in their organisation:

Identifying suitable use cases

Consulting firms can work closely with their clients to identify areas where LLMs can add the most value when solving business problems. In particular, businesses need to recognise what unique data sets they already have, such as customer data, financial data and more. These data sets are an organisation’s differentiator from their competitors, and their key ingredients for success when adding value to their organisation.

This can be leveraged to build a generative model to do something that no other business can.

When working with AI use cases, businesses should focus on those that can benefit the business, taking into account the resources and time for production. Start with a small-scale proof of concept which focuses on one specific task to help boost efficiency, which can provide a huge ROI. Consulting firms can help businesses select a use case that aligns with their defined objectives and success metrics.

The learnings taken from the small proof of concept can then be applied to larger or more complex projects.

Finding the right technology fit

It is often up to consultants to advise their clients when evaluating different LLM options available. The rise of generative AI has reminded enterprises that there is incredible value in using their own data sets. By helping businesses build their own LLM, they no longer need to hand over their data to third party companies, while they also benefit from having more control over model quality, cost and desired behaviour. This can better line businesses up for long term success.

A second element for consultants to consider when advising their clients is where the organisation’s data is stored. If data is in silos, then businesses will struggle to build LLMs as their data will be split across disparate systems. Emerging data intelligence platforms, built on data lakehouse architecture, not only provide direct file access and direct native support for data science but also make data much easier to query, manage and govern.

Leveraging such platforms significantly boost businesses’ efficiency when developing their own applications and LLMs.

Training and support

Those employees interacting with the LLM need training and a thorough understanding of how the model works. This includes familiarising themselves with the capabilities and limitations of LLMs. Though encouraging experimentation and a culture of learning, consultants can effectively provide guidance on how businesses can leverage LLM capabilities.

Change management is a structured approach to support teams through a planned change. This is crucial when introducing new technologies like LLMs. Consultants can play an important role here, creating awareness and buy-in among employees. It is also important to provide training to users on how to effectively interact with LLMs. Consultants can actively recommend introductory LLM courses to employees to help build their expertise in the area.

Evaluation of progress

Evaluating the effectiveness of the LLM is equally as important as the implementation, and as such, their performance needs consistent monitoring. Consultants can help enforce this, while providing recommendations to improve the impact of LMs in real-world scenarios.

Consultants should encourage businesses to regularly collect feedback from users. They need to make sure clients stay on top of their KPIs, and help identify areas for improvement. Consultants can also share recommendations to iterate and fine-tune the models. This can help boost the performance of the LLM.

As LLMs are bound to define the future of AI, it is critical that consulting firms encourage their clients to protect their proprietary data to set them up for success. A company’s data is its most valuable asset and key differentiator from its competitors. By bringing expertise, guidance and support, consultants can help clients effectively build their own LLMs using the right tools and technology.

In doing so, organisations can unlock the full potential of LLMs across the business without feeding their data to the models owned by larger corporations.

About the author: Greg Taylor is Vice President for Consulting and Systems Integrator Partners for Asia Pacific and Japan at Databricks.