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It goes without saying that Conversational AI leverages technology. In fact, different technologies, platforms and applications can greatly influence how you achieve your goals with a Conversational agent.
Declarative AI: AI Assistants use rule-based systems and machine-learning algorithms to interpret user queries. They typically follow predesigned conversational flows or dialogs, focusing on specific tasks and intents. While effective for structured conversations, they can struggle with complex or ambiguous queries.
Generative AI: AI assistants powered by large language models (LLMs) like GPT-4, leverage vast amounts of text data to generate human-like responses. They can interpret context, adapt to different conversation styles, and provide more comprehensive and informative responses. However, they can sometimes produce inaccurate or misleading information, especially when dealing with factual queries or sensitive topics. Also creating turn by turn conversations, where each turn is mission critical is a challenge.
Hybrid assistants: typically offer the best of both worlds: the flexibility of LLMs and the reliability of rule-based business logic. This often requires a rethinking of the conversational AI solutions that you currently have in place and how you want to scale them going forward.
Ultimately, every AI assistant is powered by different technologies, depending on the use cases, channels, and modalities that are supported.
Speech recognition: Converts spoken language into text for processing by the AI.
Text-to-speech: Synthesizes text into spoken language for output.
Speech Synthesis Markup Language (SSML): allows for synthesized speech to be customized and for sound effects and similar to be added.
Machine learning: Trains models to recognize patterns and make predictions.
Deep learning: A subset of machine learning that uses neural networks to process complex data.
By combining these technologies, and using proven conversation design techniques, Conversational AI can provide increasingly sophisticated and engaging user experiences.
Deciding whether to buy or build a Conversational AI solution depends on various factors, including your organization's resources, technical expertise, and specific requirements.
Buying a pre-built solution from established vendors can offer advantages like as faster implementation, robust features, and ongoing support. These solutions typically come with a range of functionalities, including natural language understanding, dialog management, and integration capabilities, allowing you to leverage the expertise and infrastructure of the vendor.
On the other hand, building your own Conversational AI solution may provide greater flexibility and customization options tailored to your unique needs. This approach requires significant investment in terms of time, resources, and technical talent but offers full control over the development process and allows you to create a solution perfectly aligned with your business goals.
Ultimately, the decision between buying and building depends on factors such as:
Organizational resources - Evaluate internal expertise and capacity to develop Conversational AI solutions.
Timeline and budget - Consider the time constraints and financial resources available for Conversational AI development.
Technical requirements - Assess the specific needs and requirements of the Conversational AI solution.
Integration with existing systems - Evaluate the ease of integrating Conversational AI with existing IT infrastructure.
Future scalability - Assess the potential for future growth and expansion of Conversational AI capabilities.
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