ChatGPT’s November 2022 launch was a watershed moment. Within days, a million users started interacting with the app. By the end of its first year, OpenAI‘s model had attracted over 100 million enthusiasts, making it one of the fastest-growing consumer applications in history. This explosive evolution revealed the potential of Generative AI, the technology behind ChatGPT’s abilities. It can create new text, images, and audio from existing data. Conversational AI platforms quickly recognized this opportunity.
As platforms are starting to offer a wide range of Gen AI systems, selecting the one that’s best for your use case can be overwhelming. To help you choose the best option, let’s jump into the key characteristics of different model categories.
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Comparison of Popular Models
The rapid evolution of Generative AI has produced a diverse range of solutions, each with its own strengths, weaknesses, and ideal use cases. Most conversational platforms either develop proprietary models or offer a selection of industry-leading ones. To effectively compare these systems, we’ve categorized them into two groups:
Cost-Effective Models: GPT-3.5 Turbo (OpenAI), PaLM 2 for Text (Google), LLaMA 3 (Meta), Claude 3 Haiku (Anthropic), GPT-4o Mini
Advanced Models: GPT-4o (OpenAI), Gemini (Google), LLaMA 3 (70B) (Meta), Claude 3.5 Sonnet (Anthropic)
Choosing the right one depends on your business needs, budget, and the complexity of tasks you’d like to automate. The price of a model often reflects its power. More expensive options generally handle complex tasks better. They can process more information at once, leading to better answers. However, these capabilities come at a cost: speed. Cheaper tools are quicker but might struggle with complex queries.
How Conversational AI Platforms Integrate Generative Capabilities
So far, the strategy of most platforms has been to minimize the Gen AI drawbacks, such as inaccurate outputs, hallucinations, and delays in response times, while maximizing the benefits. One key area where generative algorithms are making a significant impact is speeding up AI assistant creation.
- Automating Data Creation: Platforms like Cognigy use Generative AI to automatically create data for training digital helpers. By simply providing basic information, developers can craft large datasets of potential user phrases that enable conversational tools to understand inputs.
- Natural Language Bot Building: Amazon Lex allows developers to describe the AI assistant’s purpose and capabilities in plain language. The platform then generates the underlying technical components, which results in reduced development time.
- Intelligent Intent Recognition: Voiceflow takes this a step further by using advanced language models to automatically identify user intents based on their requests. This means conversation designers can focus on crafting responses rather than manually defining every possible user question.
Generative AI is also enhancing how bots interact with users:
- Enriching Answers: LivePerson combines the power of knowledge bases with GAI to provide more informative and engaging responses. By understanding the context of a query, the system can deliver tailored and comprehensive answers.
- Summarizing Conversations: Tools like Kore AI use Generative AI to create summaries of customer interactions. This helps agents quickly understand the conversation history and provide better support.
To summarize, the technology has enhanced the functionality of Conversational AI platforms and provided:
- More accurate and human-like bot responses;
- Improved natural language understanding;
- Faster bot building;
- Automation of simple support tasks, freeing up customer service agents to focus on more complex issues.
Check out our extended Comparison of Conversational AI vs. Generative AI
Leveraging AI with Master of Code Global
To effectively implement these innovations, expertise in integration is essential. At Master of Code Global, we specialize in connecting Conversational AI platforms with third-party systems to gather customer information and offer a personalized omnichannel experience. OpenAI’s solutions, among others, can be embedded into the conversational flow, starting with user queries and ending with AI-generated responses.
We are already working on adding ChatGPT-like functionality to the existing bots of our clients. So, we do not need to throw away old flow-based bots and replace them with a new generating-AI base one. We can augment existing bots with the GPT-based flows on a custom fine-tuned GPT model. This model can keep the focus on business-case-specific knowledge and not try to answer every question as generic ChatGPT does.
We focus on building conversational solutions for both voice and text within best practices for Conversation Design. MOCG experts integrate new technologies into existing systems and work closely with stakeholders to assess the feasibility of deploying Conversational AI solutions. This includes selecting the appropriate technology, identifying data sources, and determining necessary integrations to ensure the best user experience.
Ready to build your own Conversational AI solution? Let’s chat!