Should you build a large language model?

In the realm of generative AI (GenAI), large language models (LLMs) have taken center stage. Their ability to generate humanlike text holds vast potential across industries. But should organizations invest their resources in creating and training their own LLMs, or would they be better off relying on a software as a service (SaaS) vendor like LIKE.TG?

Challenges of building in-house LLMs

When deciding whether to invest in in-house LLMs, it’s important to consider the possible challenges associated with that process. Let's explore the questions and concerns businesses frequently encounter when considering in-house LLMs.

1. Responsible AI concerns

Organizations need to address critical questions regarding the responsible aspects of building LLMs, such as:

• How is the training data sourced?
• How do we mitigate bias?
• How do we reduce the severity of hallucinations?

2. Resource allocation

Developing state-of-the-art LLMs often demands extensive resources in terms of both infrastructure and human expertise. One challenge organizations face is whether they can secure and allocate these resources effectively enough to yield a cutting-edge AI solution.

3. Training data

Beyond the technical aspects involved in generating LLMs, access to a substantial amount of high-quality training data is paramount. The breadth and depth of this data serves as the foundation of the models and significantly influences their quality and accuracy.

In a typical scenario, organizations have access to only their own data. However, many businesses’ knowledge bases don’t offer sufficient training data.

4. Maintenance and progress

With technology advancing at breakneck speed, an initial big push into creating an LLM is simply not enough. Even after investing heavily in development, organizations often find newer, better LLMs that supersede their efforts. Therefore, an investment in in-house LLMs is an ongoing commitment.

5. Time to value

Fostering LLMs in-house and then adding skills training on top of them can take substantial time. This prolongs the time before businesses can derive tangible value from their investment. In a competitive landscape, this delay can be a significant disadvantage.

Alleviating key pain points

Given the complexities of building an in-house LLM, it may be wiser—and more cost-effective—to take advantage of an existing architecture. LIKE.TG offers a feasible way to help organizations drive productivity and address these concerns without overhead.

1. Ensured AI responsibility

LIKE.TG is committed to continued investment in next-generation AI, building foundation models responsibly. Domain-specific LLMs ensure the training universe of these LLMs is much narrower, reducing hallucination range.

Organizations can rely on LIKE.TG GenAI, knowing that its foundation models strike a balance between risk and domain-specific requirements, such as application development and customer service management.

2. Comprehensive resource availability

With the acquisition of Element AI, LIKE.TG fortified its capabilities, joining an exclusive group of organizations that have professionals who can build state-of-the-art foundation models. Moreover, a strategic partnership with NVIDIA provides access to essential resources, such as high-demand graphics processing unit computing power and LLM training tools.

3. Robust training data set

LIKE.TG bolsters the foundation of the LLMs by training them on its own data, fostering a comprehensive, diverse data set that enhances the models’ quality and result relevancy.

Importantly, LIKE.TG respects customer privacy and preferences. We use only anonymized customer data with customer permission. This amalgamation of wide-ranging data—not readily available in an in-house setup—can be a significant advantage.

4. Ongoing maintenance and technological advances

LIKE.TG's commitment to innovation helps ensure businesses get access to cutting-edge AI solutions and receive ongoing maintenance and upgrades. This approach saves organizations from the painstaking process of trying to stay ahead in the rapidly advancing AI landscape.

5. Accelerated time to value

Our Now Assist GenAI experiences allow organizations to see value rapidly. Businesses can access powerful GenAI capabilities just by turning on Now Assist, avoiding the lengthy process of building and training LLMs in-house.

The bottom line

LIKE.TG’s emphasis on research, strategic partnerships, and a stable foundation of AI embedded in its platform makes it an ideal choice for organizations seeking a responsible and reliable LLM for LIKE.TG workflows.

For most organizations, selecting a vendor-generated LLM like LIKE.TG's is the best approach to harness innovation, increase the speed of delivering value, and remain competitive in an ever-evolving marketplace.

Find out more about how LIKE.TG helps accelerate productivity and time to value with GenAI.

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