AI Conversational Agents: Everything You Need To Know

Many organizations are eager to adopt AI for real-time agent support. AI conversational agents offer key benefits like boosting efficiency and cutting costs 87% of organizations wish to leverage AI to provide real-time agent assistance. It’s not hard to see why. AI conversational agents unlock a suite of competitive advantages for your business – such as improved efficiency and cost-cutting – as we’ve discussed in our recent article.

AI conversational agents are revolutionizing customer support by offering real-time assistance, cutting costs, and enhancing user experience. But before diving into AI, you might have some lingering questions about its implementation. Our experts have gathered FAQs from past AI projects below so that you can get your initial doubts cleared up and make informed decisions.

FAQs About AI Conversational Agents

1. How Is a Conversational Assistant Developed?

Developing an AI conversational agent can be done in several ways. The most popular method is using an LLM (Large Language Model), which calculates responses based on the probability of combining the best set of tokens.
One downside of an LLM is that it can sometimes generate incorrect responses when it doesn’t know the answer, a phenomenon known as “hallucination.” This can be mitigated by customizing the AI to better understand and respond accurately. Partnering with an experienced team can involve interconnecting modules to ensure the question is answered more accurately.
Additionally, you can train your AI model using your own Question/Answers dataset or engage in “prompt engineering.” In this case, you create instructions for the agent to act in a certain way and recognize when it doesn’t know the answer.

2. What Types of Conversational Agents Can Be Developed?

There are several types of AI conversational agents, each suited to different needs:

  • Generative Chatbots: These leverage advanced language models to generate responses based on the conversation’s context. They adapt to the user’s query in the moment, without relying on a predefined set of responses.
  • RAG (Retrieval Augmented Generation): This type responds to the user based on the context learned from a source. For example, you might train your RAG using files, texts, or a vector database.
  • Hybrid AI Chatbots: These combine generative and retrieval capabilities, generating responses and retrieving relevant information from databases or external sources, depending on the query.
  • Personalized Chatbots: These learn from previous interactions, adjusting their responses to provide a more personalized experience. They’re typically given a set of training questions and answers to learn from.
  • Task-Oriented Chatbots: Designed to handle specific tasks like booking an appointment, making a purchase, or finding specific information.

3. What Platforms and Channels Can the Conversational Assistant Be Integrated With?

AI conversational assistants can be integrated wherever users chat. This includes tools such as Slack, Discord, webpages, PC consoles/terminals, and more.
Additionally, you can develop voice-based agents for platforms that support speech interaction. In these cases, the model translates the text, responds via text, and then plays back this text on a speaker.

4. How Is the Assistant Customized to Align With My Company’s Brand?

Both Generative AI and RAG can achieve brand-specific responses. GenAI bases its answers on a certain context, while RAG retrieves the best-fit data from a “Questions and Answers” dataset. A hybrid model can also be leveraged for a more aligned brand voice.
No matter the exact model, an AI partner will ensure it’s customized to your parameters, so the AI reflects your brand’s tone and values.

5. How Long Does It Take to Develop and Implement a Custom Conversational Assistant?

The development timeline depends on the complexity of the project. For example, a simple “hello world” chatbot can take just an afternoon to develop.
However, more complex agents may take several weeks or even months to achieve a well-rounded product. This is especially true if the agent involves gathering information, integrating with platforms, sending emails, and beyond.

6. What Kind of Maintenance and Updates Does a Conversational Assistant Require?

AI models are constantly being updated. For instance, OpenAI’s GPT has released four versions in the last three years.
However, this doesn’t mean your agent needs frequent updates. Sometimes a model works as is, and updating it can lead to inconsistencies. Regularly analyzing the conversations your agent generates allows you to spot responses that aren’t 100% accurate and identify ways to improve its performance. This often involves tweaking the prompts or updating the training dataset.

7. What Costs Are Associated With Developing a Custom Conversational Assistant?

To start, you will have to invest in development costs, as well as services to keep your AI app running (hosting, server, storage, database, etc.).
What’s more, you’ll have to pay for the processing of tokens. Every model – such as Chat GPT, OpenAI, or Gemini – consumes tokens to process the input and generate the output. These tokens have an associated cost depending on the model’s processing power. (One exception is the open-source model LLaMA, which allows for free token processing.)

8. How Is the Success of a Conversational Assistant Measured?

Success will vary according to your specific business goals and KPIs. However, as a whole, it’s important to achieve an agent that:

  • Meets the chatbot’s intended purpose
  • Minimizes hallucinations and bias
  • Executes required tools on demand (only when needed)
  • Fulfills other goals as defined in the project scope (i.e., speed or cost per inquiry)

9. What Are Best Practices for Building an AI Conversational Agent?

Your chosen AI development partner should be well-versed in best practices. Some major areas include:

  • Understanding your customer pain points
  • Integrating AI with your legacy systems and tech stack
  • Making AI consistent across multiple customer channels
  • Personalizing AI according to your buyer personas
  • Designing brand-specific conversations and a human agent hand-off
  • Leveraging visuals, including icons, buttons, message chunking, etc.
  • Setting up chatbot metrics for streamlined team use
  • Achieving accessibility and inclusivity
  • Safeguarding against bias (race, gender, accent, age, region, etc.)
  • Training and updating your AI algorithm regularly

Choose OrangeLoops for Top AI Expertise

Developing an AI conversational agent can be a complex process. Yet, it quickly pays off to have a well-designed agent that can handle a large segment of your customer inquiries.

By checking out these FAQs above, we hope you’re better informed about getting started with AI. Of course, you may have more questions along the way – don’t hesitate to ask your AI partner!

Ultimately, choosing the right AI development partner can be decisive for your project success. At OrangeLoops, we have a dream team of AI specialists ready to discuss your chatbot vision. Our experts are at the cutting edge of AI trends and techniques to ensure your AI conversational agent is truly industry-best. Get in touch with us today!

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