November 30 marks one year since ChatGPT was introduced to our lives. Since then, large language models (LLMs) have been ushering in a whole new era of marketing innovation. And as brands increasingly rely on AI for content generation, they are uncovering newfound limitations, and are now faced with two options:
- Use a 3rd party solution
- Develop an in-house solution using commercial or open-source LLM
Phrasee, a leading innovator in brand language optimization, just released a new white paper “The Definitive Guide to Large Language Models and High-Performance Marketing Content,” on how enterprise marketers can build an in-house LLM solution and use it at its full potential.
Toby Coulthard, CPO at Phrasee, explains:
“There is uncertainty about the long-term capabilities of large language models. LLMs have to scrape the internet and learn from copyright data. As we go through more litigations in the space, the database will start to grow smaller, limiting LLM performance. If you are basing your technology on one model, it may encounter challenges, such as being down for an hour or having these regulated capabilities. Yet despite these challenges, it’s true that LLMs provide creative value and save time on generating content.
Many companies who depend on one model have to face the pitfalls of an LLM plus the costs on top of it. It’s important to have control, reliability, and scalability within your content generation. The solution is being model agnostic, with a hybrid approach to creating the best tools for a job. That might mean having multiple LLMs to mitigate the pitfalls of one, or having rule-based natural language generation that might be less creative, but also doesn’t hallucinate.”
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