Cofounder and CEO of Nurdle AI, which generates privacy-safe custom datasets on demand that make building AI faster, cheaper and easier.
In a previous article, I spoke about the challenges of rolling out generative AI applications and the steps businesses can take to launch cost-effective AI applications that deliver value quickly. In this article, I want to dig deeper into a concept I mentioned in the first article: the benefits of specialized or fine-tuned models.
Many B2B AI companies are exploring ways to fine-tune their large language models (LLMs) to expand their platforms’ functionality to include features like AI-produced sales and marketing materials, emails, blog posts and social media posts.
LLMs hold a great deal of promise in this use case, but they need specialization. This article looks at this specialization.
The Role Of Specialized Models
We’ve all seen instances of brands eager to deploy AI-powered chatbots without investing in training these systems with highly specialized, brand-specific data. This haste has occasionally led to disastrous outcomes and underscores the importance of a meticulously tailored approach to developing AI chatbots.
General LLMs like ChatGPT and Bard have been trained on a huge knowledge base rather than specific domain content, which can result in hallucinations and bias. They’re also expensive to put in production, as I previously discussed.
Digging deep into, say, selecting which brand’s shoe model is right for your foot or listening to your customers describe their confusion with their bill requires highly specialized knowledge. This is not a criticism of ChatGPT or Bard in any way. It’s simply a recognition that, for specific brand AI use cases, specialized models are a requirement.
Such models are fine-tuned to the brand’s knowledge base and brand voice so that they can understand and interact with employees, customers and prospects in ways that are aligned with the brand’s specific needs. This requires fine-tuning on industry terminology, brand products and IP as well as user expectations.
Specialized models are better able to understand and generate content related to particular concepts accurately—content that may not be universally recognized or well-understood by more general language models.
For this reason, leveraging such specialized LLMs could give an edge to businesses keen to use LLMs for very specific functions and use cases based on their own data.
The Role Of Brand Data In Training A Specialized LLM
The accuracy of all AI models—LLMs included—is dependent on the quality of data on which it’s trained. Specialized LLMs deliver two key benefits: They lower the production cost and make the model more accurate by fine-tuning them with domain-specific models.
When training a specialized LLM, it’s important to leverage specific datasets relevant to the brand’s specific use case. If the use case is to deploy an LLM to create an email nurturing campaign or to respond to requests for more information, the model should be trained on marketing emails, FAQs, product briefs, social media posts and the like.
This data helps the model grasp the nuances of the brand’s offerings and the specific language its customers use, leading to better engagement and satisfaction. Training them on specific data in the brand voice will make the model more efficient at processing and using data for customer interactions. This targeted approach allows for a more accurate understanding of customer needs and preferences.
Brand-specific data will also help the LLM personalize interactions based on customer behavior, preferences and previous interactions with the brand. This level of personalization can improve customer experiences, increase loyalty and boost conversion rates by making each interaction feel unique and tailored to the individual customer.
Specialization can also ensure that communications encompass any compliance requirements and privacy considerations that may apply to the brand’s specific industry.
The challenge with specialized models, however, is getting access to a sufficient dataset to properly train it. This may be a particularly acute issue if existing data (e.g., customer service email messages) have PII or sensitive data contained within it.
When real-world data is scarce, synthetic data can take its place.
Scaling Training Data With Synthetic Data
Synthetic data is relatively inexpensive in that it doesn’t need to be scrubbed and annotated by humans, and it is privacy-compliant.
It also helps the AI app get to value faster because the training data is optimized for the use case. Synthetic data allows for quicker iteration on model training and refinement, speeding up the development and deployment of AI solutions.
Synthetic data will also allow for a greater array of data at a much lower cost that might not exist within a brand’s real-world data. This is useful for brands expanding their categories of products or introducing products and services that are adjacent to their existing offerings. Training models on synthetic data can introduce a wider variety of examples than might be available in the brand’s actual datasets, including edge cases.
The world has learned a lot since the introduction of ChatGPT. It can be a powerful driver of efficiency as long as it is implemented correctly. For marketing communications and marketing automation use cases, specialized models trained on brand-specific data will deliver the best results at the best cost.
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