Large Language Models in Publishing
For industries with large amounts of textual data, generative AI is having a fundamental impact. Large language models (LLMs) are taking the publishing industry to the next level with their ability to process, generate, and adapt content.
Improve customer engagement
Generate new revenue streams
Enhance the subscriber experience
Optimize existing content for greater insights
Accelerate content creation and internal productivity
Build an AI muscle
Top AI-Driven Opportunities in Publishing
Online Media
By incorporating LLM technology into their products, online media companies can personalize content and create the interactive experience that customers expect. Internally, LLMs act as co-pilots for writing, editing, and organizing information.
Market Research
Research firms collect data and produce industry reports. LLMs can analyze this data and extract insights for significantly quicker turnarounds. They make the analyst's job easier by identifying current trends and patterns.
University Presses
AI-powered interfaces make it easier for students and researchers to navigate university press archives and discover new knowledge. Internally, LLMs can streamline manuscript review for compliance with university style guides.
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Frequently Asked Questions
While there are companies who have gone the route of training their own LLM from scratch - spending millions of dollars in the process - it is actually a better idea to remain flexible if you want to stay competitive. Vendor agnosticism, a principle championed by deepset, allows you to change models when they no longer serve you. For example, if a cheaper or faster model comes along, you can simply plug it into your existing pipeline and move on.
Keeping data safe, especially sensitive customer or business data, is a big concern in the age of AI models and decentralized computing infrastructures. At deepset, we recognize this and have therefore prioritized data security. Users can manage access with MFA and SSO in deepset Cloud, while our virtual private cloud (VPC) option provides the flexibility to leave their data layer in their preferred location. Furthermore, we are SOC 2 and GDPR-compliant, as well as CSA STAR Level 1 certified.
To build products or internal tools with AI, you need to put together a team that understands AI technology, has a product mindset, and understands both user needs and business requirements. This type of team is called an "AI team." It can be large or small, as long as it has the right cross-functional skills. To learn more about AI teams and how to build one, check out our whitepaper on Leading a successful AI team.
LLMs "hallucinate", that is, they make up facts that are not supported by any data. Because of the eloquence of these models, hallucinations can be difficult to detect, creating a volatile factor that is a barrier to using LLMs in production. However, using a combination of prevention techniques, teams can reduce the number of hallucinations to a minimum. These include effective prompting, grounding the LLM's response in fact-checked data through retrieval augmented generation (RAG), and monitoring the Groundedness of responses.
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