Launching New Generative AI? Four Principles Critical to Success
February 27, 2023
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Delight Customers with Impactful Experiences
Generative AI is poised to radically open a world of new opportunities to delight customers across every industry. From conversational chatbots that deliver personalized responses, to code for various applications, to targeted content for marketing communications, generative AI is revolutionizing the way business is done. In the race to deliver exceptional customer experiences, companies need to build trusted generative AI applications that work well in the real world and are consistent with their brand voice.
When developing a generative AI project, it’s important to consider the following:
Trust is the Foundation of Loyalty
Customer loyalty is hard-earned and fragile. It can take years to build a strong relationship with your audience and only seconds to destroy it. Although generative AI offers immense possibilities, it’s crucial to preserve customer trust while creating new products and experiences.
Generative AI is ushering in a host of possibilities for enhancing customer experiences in every industry. However, it’s not without risks to brand integrity. In certain cases, the patterns identified by the model may result in AI-generated hallucinations. It’s critical to involve human feedback to ensure helpful, honest, and harmless interactions.
Essentially, you want your AI model to solve problems in a helpful and accurate way. Companies build trust with consistency, honesty, and accuracy and by not causing any harm with poor or inappropriate content. Done properly, generative AI delivers experiences where bias and toxicity are absent.
Quality Builds Richer User Experiences
Generative AI has the potential to provide engagements that drive greater customer satisfaction. This is because generative AI applications can be fine-tuned to a person’s unique interests, needs, and preferences facilitating next generation personalization.
Human feedback is an integral component of building quality generative AI applications. The power of humans is their ability to question the information presented to them – to tell an AI system when it’s being dishonest, unhelpful, or harmful. Reinforcement Learning with Human Feedback (RLHF) can provide more accurate predictions and recommendations based on relevant and reliable information.
Diversity Broadens Your Reach While Personalizing for Each User
Real-world representation in your models is critical to meet customer expectations and strengthen your brand. Advances in AI are pushing consumers to expect to see themselves mirrored in the technology they use. Personalized experiences have become the norm, and to guarantee individualized interactions for every customer, it’s crucial to use diverse data to ensure the AI model is mindful of social context.
Continuous Feedback Ensures Your AI Evolves at the Pace of Humans
Businesses that use generative AI applications can effectively gain a competitive advantage. Continuous feedback loop ensures your AI applications evolve as the world changes. Keeping your AI model up to date with new data and information is important for personalization, relevance, and consistency.
Generative AI applications can be incredibly powerful tools. Many of these applications, however, require a significant amount of technical knowledge and expertise to develop effectively and at scale. Having a trusted partner to handle the continuous influx of data on a centralized platform simplifies processes, workloads, and the ongoing evolution of your generative AI applications.
Build Generative AI with Appen's Comprehensive & Scalable Products
The four principles required to build trustworthy generative AI: Trust, Quality, Diversity, and Continuous Feedback, will help you transform customer engagements from transactions to experiences. Today, we are combining our industry-leading platform, depth of expertise and unmatched global team of AI Training Specialists to enable companies to launch successful generative AI applications.
Reinforcement Learning with Human Feedback
Though large language models are very powerful, they don’t always produce results that are accurate or aligned with human values. Even worse, the results can be biased or toxic. To tackle the risk of bias and hallucinations, large language models need human reinforcement. Our AI training specialists engineer high-quality prompts and responses to empower your fine-tuning and alignment effort toward relevant and ethical outputs.
Real-world representation of people of all genders, ethnicities, ages, languages, social, religious, and cultural backgrounds result in the least biased solution for your generative AI applications. With a diverse team of global AI Training Specialists from a variety of professions such as medical, linguistics, creative writers, and coders, your AI models are tuned with the most relevant and accurate feedback in your industry.
With deep expertise in language processing and experience collecting and annotating millions of documents for industry leaders around the world, we are your trusted partner for document intelligence. Businesses have a wealth of unstructured data in the form of scanned and photographed documents of all kinds. By extracting the insights from this data, they can deliver new innovative experiences for their customers. Our clients can now make any document a usable data source without worrying about specific document formats or templates. With exceptional results of 99% accuracy on diverse documents, our clients are launching new products and expanding into additional markets. To learn more about our Document Intelligence product, visit Unlocking Insights with Document Intelligence.
Automated NLP Labeling
Time to market is top-of-mind for our clients, which is why speed of delivery is built into our solution. We use the zero shot or few shots learning technique and the generative AI large language model to automatically annotate data, saving our clients considerable time. To ensure quality is not compromised and to accurately complete the tasks, our AI training specialists review the annotated dataset and update labels as needed.