Fine tuning involves feeding the model labeled data specific to the content generation application—questions or prompts the application is likely to receive, and corresponding correct answers in the desired format. For example, if a development team is trying to create a customer service chatbot, it would create hundreds or thousands of documents containing labeled customers service questions and correct answers, and then feed those documents to the model.

Fine-tuning is labor-intensive. Developers often outsource the task to companies with large data-labeling workforces.

In RLHF, human users respond to generated content with evaluations the model can use to update the model for greater accuracy or relevance. Often, RLHF involves people ‘scoring’ different outputs in response to the same prompt. But it can be as simple as having people type or talk back to a chatbot or virtual assistant, correcting its output.

Gen AI tools can inspire creativity through automated brainstorming—generating multiple novel versions of content. These variations can also serve as starting points or references that help writers, artists, designers and other creators plow through creative blocks.

Generative AI excels at analyzing large datasets, identifying patterns and extracting meaningful insights—and then generating hypotheses and recommendations based on those insights to support executives, analysts, researchers and other professionals in making smarter, data-driven decisions.

In applications like recommendation systems and content creation, generative AI can analyze user preferences and history and generate personalized content in real time, leading to a more tailored and engaging user experience.

Generative AI operates continuously without fatigue, providing around-the-clock availability for tasks like customer support chatbots and automated responses.

An AI hallucination is a generative AI output that is nonsensical or altogether inaccurate—but, all too often, seems entirely plausible. The classic example is when a lawyer used a gen AI tool for research in preparation for a high-profile case—and the tool ‘produced’ several example cases, complete with quotes and attributions, that were entirely fictional (link resides outside ibm.com).

Some practitioners view hallucinations as an unavoidable consequence of balancing a model’s accuracy and its creative capabilities. But developers may implement preventative measures, called guardrails, that restrict the model to relevant or trusted data sources. Continual evaluation and tuning can also help reduce hallucinations and inaccuracies.

Due to the variational or probabilistic nature of gen AI models, the same inputs can result in slightly or significantly different outputs. This can be undesirable in certain applications, such as customer service chatbots, where consistent outputs are expected or desired. Through prompt engineering—iteratively refining or compounding prompts—users can arrive at prompts that consistently deliver the results they want from their generative AI applications.

Generative models may learn societal biases present in the training data—or in the labeled data, external data sources, or human evaluators used to tune the model—and generate biased, unfair or offensive content as a result. To prevent biased outputs from their models, developers must ensure diverse training data, establish guidelines for preventing bias during training and tuning, and continually evaluate model outputs for bias as well as accuracy.

Many generative AI models are ‘black box’ models, meaning it can be challenging or impossible to understand their decision-making processes; even the engineers or data scientists who create the underlying algorithm can understand or explain what exactly is happening inside it and how it arrives at a specific result. Explainable AI practices and techniques can help practitioners and users understand and trust the processes and outputs of generative models.

Assessing and comparing the quality of generated content can also be challenging. Traditional evaluation metrics may not capture the nuanced aspects of creativity, coherence, or relevance. Developing robust and reliable evaluation methods for generative AI remains an active area of research.

Generative AI models can be exploited to generate convincing phishing emails, fake identities or other malicious content that can fool users into taking actions that compromise security and data privacy. Developers and users need to be careful that data put into the model (during tuning, or as part of a prompt) doesn’t expose their own intellectual property (IP) or any information protected as IP by other organizations. And they need to monitor outputs for new content that exposes their own IP or violates others' IP protections.

Deepfakes are AI-generated or AI-manipulated images, video or audio created to convince people that they’re seeing, watching or hearing someone do or say something they never did or said. They are among the most chilling examples of how the power of generative AI can be applied with malicious intent.

Most people are familiar with deepfakes created to damage reputations or spread misinformation. More recently, cybercriminals have deployed deepfakes as part of cyberattacks (e.g., fake voices in voice phishing scams) or financial fraud schemes.

Researchers are hard at work on AI models that can detect deepfakes with greater accuracy. In the meantime, user education and best practices (e.g., not sharing unverified or unvetted contentious material) can help limit the damage deepfakes can do.