Solving LLM Hallucinations in Conversational, Customer-Facing Use Cases

In a recent meeting with technical leaders from a large enterprise, we discussed Parlant as a solution for developing fluent yet tightly controlled conversational agents. The discussion took an unexpected turn when someone posed the question: “Can we use Parlant while turning off the generation part?”

Initially, this question seemed paradoxical. How could a generative AI agent function without generation? However, upon deeper reflection, the rationale became clearer.

The High Stakes of Customer-Facing AI

For these teams, the stakes were high. Their AI agents were set to engage directly with millions of users monthly. In this context, even a 0.01% error rate is unacceptable; one in ten thousand incorrect interactions poses risks of compliance failures, legal implications, or damage to brand reputation.

“Pretty good” isn’t adequate in this environment. While LLMs have advanced significantly, their free-form generation can introduce uncertainty, including hallucinations, unintended tones, and factual inaccuracies.

A Shift in Perspective

The inquiry about disabling generation was not born from ignorance, but rather from a desire for control. These organizations employed full-time Conversation Designers, professionals skilled in crafting interactions that align with brand voice, legal requirements, and customer engagement strategies.

These teams sought to turn off generation not from a place of fear, but from a need for certainty in customer interactions. This realization challenged the conventional understanding of generative AI. It is not merely about open-ended, token-by-token generation; rather, it is about adaptive responses that ensure appropriateness—compliance, contextual relevance, clarity, and usefulness.

The Hidden Key to the Hallucination Problem

As enterprises strive to mitigate output hallucinations, a key solution already exists: Conversation Designers. By integrating these professionals into the development process, organizations can not only reduce hallucinations but potentially eliminate them altogether.

Conversation Designers bring clarity and intentionality to customer interactions, crafting a voice that resonates well beyond what LLMs can achieve independently. Instead of retrofitting generative systems with temporary fixes, it is more prudent to incorporate these insights into the design of platforms like Parlant.

From Insight to Product: Utterance Matching

This line of thinking led to the development of Utterance Templates within Parlant. These templates allow designers to create context-aware, fluid responses that are fully vetted, versioned, and governed.

Utterance Templates function through a three-stage process:

• The agent produces a draft message based on situational awareness (interaction, guidelines, tool results, etc.).
• It matches this draft to the closest template available in the utterance store.
• The system renders the matched template, incorporating variable substitutions where necessary.

This hybrid model empowers software developers to build reliable agents while enabling business and interaction experts to define agent behavior effectively.

Conclusion: Empower the Right People

The future of conversational AI hinges not on removing people from the loop but on empowering the right individuals to shape and refine AI communications. With Parlant, the ideal candidates are those who understand your brand, your customers, and your responsibilities best.

Ultimately, the notion of turning off—or significantly controlling—generation in customer-facing interactions is not absurd; rather, it represents how conversational AI should be developed.

Disclaimer: The views and opinions expressed in this guest article are those of the author and do not necessarily reflect the official policy or position of Marktechpost.