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AI Technology

Context, Instruction, and Example

M
M.W.
Nov 15, 20245 min read

The users of Aissist see approximately 80% in resolution rate, leaving only about 20% of cases to be escalated to human teams. This resolution rate is twice as high as what is typically observed with conventional systems. The underlying force behind this accomplishment is Aissist.io's proprietary technology based on Generative AI with Context, Instruction and Example. This innovative engine has been developed entirely from the ground up with Generative AI by the Aissist team.

The Evolution of Conversational AI

Over the last decade or so, the primary focus of conversational AI and chatbot developers has been on utilizing the "intent." Identifying a user's intent and then matching it with relevant flows, graphs or trees to drive the conversation. However, this method has shown its limitations in scalability and applicability, struggling to keep up with a wide spectrum of use cases. High-profile instances of such shortcomings can be seen in voice assistants like Siri, Alexa, and Google Assistant, which all rely on variations of intent-based frameworks. These technologies not only fall short in flexibility but also frequently fail when users deviate from their narrowly defined paths.

People have also been frequently frustrated by the chatbots which are designed with static rules and can not effectively use it to achieve the goals. Over 70% of chatbot users choose to click the “ask for human” button within 3 interactions if such a choice presents itself.

Introducing the core recipe of our AI engine

context instruction example

  1. Instruction: This component acts as a guide, outlining principles or procedures that enable the AI to perform complex business tasks. Notably, the setup process is quick and straightforward, typically taking about 10 minutes, in stark contrast to other systems that may require significantly longer timelines for implementation.

  2. Context: Unlike static flowcharts, the context component of the engine provides dynamic information that empowers the AI to tackle a vast array of tasks in numerous expressive manners. This adds a level of flexibility and robustness that traditional systems lack.

  3. Example: Scalability remains a pivotal aspect of AI success, often hampered by obscure or exceptional scenarios. The engine addresses this by learning from examples, an approach that proves more efficient than mere description, enabling the system to adeptly handle both common and rare cases.

Aissist.io's mission is to create the most powerful and accessible AI system for the rapidly expanding SMB market, with zero barriers to usage. For demonstrations and collaborative opportunities, interested parties are encouraged to reach out at sales@aissist.io.

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