As AI technology advances and becomes increasingly used amongst businesses, there are certain issues that have arisen that are now coming to light, one of them being the GenAI Chasm. Let’s explore what exactly the GenAI Chasm is, and how businesses investing in AI can cross the chasm confidently, without relying on prompt engineering.
Liran Hason, VP of AI at Coralogix coined this term after having over 2 years of experience speaking to potential customers about their GenAI products. He noticed that all businesses trying to implement AI apps struggle to get past the pilot phase, known as the chasm.
Here is an example of business ZYX that tries to cross the GenAI chasm:
Hallucinations, prompt injection risks, compliance issues, and unintended behavior are some of the main reasons that only a small percentage of apps can actually go live. Releasing an app with these issues risks damaging brand reputation, exposing sensitive information, and losing customer trust.
GenAI is an incredible tool that businesses can use to enhance their productivity and engagement with customers. However, when presenting hallucinations and incorrect behavior, most of these apps will never go live. Crossing this chasm to get AI apps to go live is a difficulty almost every business investing in AI is struggling with, but there is a solution to this situation.
One proven way to help businesses cross the chasm and release more AI apps with confidence is by implementing evaluators that sit between the LLM and the user. AI evaluators that can vet every response that comes in from the user, and that goes out from the LLM passes through these evaluators, ensuring that you are alerted to hallucinations, prompt injections, and inappropriate behavior in real-time.
While prompt engineering is currently the preferred method of mitigating hallucinations, it is not the ultimate solution that provides long-term results in the app. Studies have shown that adding more words to the system prompt decreases accuracy, making it more susceptible to hallucinations. So using prompt engineering to catch inappropriate behavior and incorrect results can only further worsen this issue as a result.
App Accuracy Decreases as More Tokens Added
Evaluators is the preferred method to use when crossing the GenAI chasm. These evaluators provide out-of-the-box policies to alert you to hallucinations and inappropriate LLM behavior. Simply integrate Coralogix’s AI observability solution and safeguard your app in a few minutes.
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