Earlier this year, we published our annual research report on AI maturity in the enterprise which included trends on generative AI deployment. 69% of respondents stated that generative AI is more important to their organizations than other AI initiatives, including 11% claiming that generative AI is much more important for their overall AI strategy. 

But it’s still early days in the deployment of generative AI and companies are experiencing several bottlenecks including security and privacy concerns, accuracy of the output, availability of high-quality training data and fine-tuning the foundation model as shown in the chart below.

Benefits of working with the right training data partner to accelerate generative AI deployments

Companies eager to capitalize on the efficiencies that generative AI delivers can accelerate their deployments and address many of these bottlenecks by working with an experienced AI data partner. This has many benefits including:

  • Generating high-quality training data 

Organizations focused on building responsible and reliable AI should train their models with high-quality, diverse datasets to improve model accuracy and inclusivity. Experienced data partners implement thorough quality control processes to optimize data integrity and reliability for their customers. They also have access to diverse groups of contributors so that the datasets they create are representative of a wide range of potential users. These contributors can also evaluate the accuracy of model outputs and provide crucial feedback for model retraining.

  • Resource and cost optimization

Outsourcing data collection and preparation allows organizations to allocate their internal resources more efficiently and focus on core activities such as product development. While companies can try to collect and label data on their own, this presents challenges that an experienced partner is equipped to handle including access to large and diverse contributor groups, and the capacity to collect and annotate high volumes of data quickly. 

  • Access to specialized expertise

Developing accurate generative AI in the realm of natural language processing requires linguistic expertise that many companies do not have access to within their organizations. Working with experienced linguists ensures that the AI can respond to human language in a nuanced and accurate manner. Some key areas of linguistic expertise include syntax and grammar, semantics, speech synthesis, part-of-speech tagging and named entity recognition. Further, a data partner can help with foundational model fine tuning to guide the model to produce the optimal output for a task based on a specific domain or context, and with prompt tuning to optimize a set of prompts that guide the model’s outputs for specific tasks. 

How LXT is helping enterprise companies create reliable generative AI

Organizations leading the charge to deploy generative AI are harnessing partnerships to accelerate and scale their deployments. LXT is helping companies on several fronts including:

  • Prompt and response creation: we’ve helped multiple companies develop high-quality inputs and outputs across several domains to enhance the quality and relevance of the generative AI application.
  • Prompt rating and ranking: we’ve also helped various organizations assess the effectiveness of prompts based on specific criteria and rank them, which also helps to improve AI accuracy.
  • Instruction fine-tuning: we’ve gathered diverse sets of input-output pairs where the input includes a clear instruction and the output is the expected response, helping to make the AI more adept at understanding and executing tasks based on given instructions.

To learn more about LXT’s generative AI capabilities and how we can help you build more accurate and inclusive generative AI, visit our services page here.