Insight Narrator : How Agentic AI prevent hallucinations in CX Management

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In an era where consumers have unprecedented access to platforms for sharing their opinions, customer feedback has emerged as a vital resource for businesses. The top online review statistics in 2024:

• 95% of customers read online reviews before buying a product?
• 89% of consumers make an effort to read reviews before buying products online
• 49% of consumers trust online reviews as much as personal recommendation
• 94% say reviews have made them avoid a business
• 97% read reviews for local businesses
• Positive reviews can increase customer spending by 31%?
• Over 81% of consumers say they are likely to check Google reviews first
• 74% of consumers say that reviews increase trust in a company

Source: Online Review Statistics: The Definitive List (2024 Data)?- Luisa Zhou

Context: The Role of AI-Driven Analytics in Extracting Insights from Customer Reviews

While the volume of customer feedback can provide a treasure trove of insights, the sheer amount of data presents a significant challenge. Traditional methods of analyzing customer reviews are time-consuming and often lack the depth required to uncover actionable insights. This is where AI-driven analytics come into play. By leveraging artificial intelligence, businesses can process vast amounts of feedback quickly and accurately, extracting meaningful insights to inform strategy, improve products, and enhance customer experiences. However, not all AI systems are created equal; some are prone to "hallucinations," where the AI generates false or misleading information, leading to potentially harmful business decisions.

Hallucinations are instances where the AI creates false, misleading, or nonsensical information that wasn't part of the input data. These hallucinations can be problematic, especially when the output is used for decision-making in critical contexts like business, healthcare, or law.

How can Agentic AI prevent hallucinations?

Agentic AI is designed to mitigate the issue of AI hallucinations through a combination of advanced techniques that ensure the reliability and accuracy of the generated content. Here’s how Agentic AI can prevent hallucinations:

1. Contextual Understanding and Grounding

• Context-Aware Processing: Agentic AI models are built to maintain a deep understanding of context throughout the interaction. By continuously referencing the conversation context or data source, the AI ensures that the generated output remains relevant and accurate, reducing the likelihood of hallucinations.
• Fact-Checking and Data Integration: Agentic AI integrates real-time data and fact-checking mechanisms into its processing pipeline. This allows the AI to cross-reference generated content with verified data sources, ensuring that the information is grounded in reality rather than purely generated from patterns in the training data.

2. Enhanced Training Techniques

• Curated and Verified Training Data: Unlike traditional models that are trained on vast, uncurated datasets, Agentic AI emphasizes the use of high-quality, curated data for training. By reducing exposure to incorrect or biased information during training, the AI is less likely to generate false content.
• Continuous Learning and Updates: Agentic AI models are designed for continuous learning, allowing them to incorporate the latest data and corrections. This helps minimize the propagation of outdated or incorrect information, a common cause of hallucinations in AI models.

3. Robust Validation Mechanisms

• Multi-Layer Validation: Before delivering outputs, Agentic AI employs multi-layer validation checks. These checks can involve statistical validation, logical consistency checks, and verification against authoritative data sources. By passing through these validation layers, the AI can catch and correct potential hallucinations before they reach the user.
• Human-in-the-Loop Systems: In scenarios where accuracy is critical, Agentic AI can incorporate human oversight into the loop. Human experts can review AI-generated content, particularly in ambiguous or high-risk situations, ensuring that the final output is reliable.

4. Explainability and Transparency

• Transparency in Reasoning: Agentic AI systems are designed to be more transparent in their reasoning processes. By providing explanations for the generated content, the AI allows users to understand the basis of the information, making it easier to identify and question potential hallucinations.
• User Feedback Integration: Agentic AI actively integrates user feedback to improve its accuracy over time. When users identify errors or hallucinations, the AI can learn from these corrections, reducing the likelihood of similar mistakes in the future.

5. Controlled Creativity

• Restrained Generative Models: Unlike traditional generative models that may prioritize fluency and creativity over accuracy, Agentic AI can impose stricter constraints on creativity in contexts where factual accuracy is paramount. This controlled approach helps in preventing the generation of imaginative but incorrect information.

6. Cross-Model Collaboration

• Ensemble Methods: Agentic AI can leverage ensemble methods, where multiple AI models collaborate to generate and verify outputs. By combining insights from different models, the system can cross-check and validate information, reducing the chances of any single model’s hallucinations affecting the final output.

In the latest article I wrote, you can find a practical example of using Agentic AI with Disneyland Paris as a case study. In this example, agents are utilized to retrieve data that has already been summarized and calculated by SQL functions (the tools used by these agents). The verified results are then passed on to either LLM or SML models to generate insightful comments and analyses.

Let's dive into a practical example

Imagine you're tasked with analyzing 130,000 customer comments about Disneyland Paris. If you simply feed all these comments into a Large Language Model (LLM) without any additional support, the LLM might generate unreliable results. For instance, when trying to determine how often certain topics or sentiments appear, the LLM might struggle with the necessary mathematical calculations, leading to flawed or inaccurate conclusions.

Now, consider the agentic AI approach. Instead of relying solely on the LLM, the task is broken down into smaller, more manageable sub-tasks. For example, before any sentiment analysis is performed, an agent specifically designed for data processing would extract the key terms from the comments. Another agent, equipped with mathematical tools, would then calculate the frequency and correlation between these terms, ensuring the results are mathematically sound.

Once these calculations are completed and verified, the results are passed on to an LLM. At this stage, the LLM can use accurate data to generate actionable insights. For instance, if the analysis reveals a strong correlation between "long wait times" and "negative reviews," the LLM can suggest targeted improvements for Disneyland Paris.

In this way, the Agentic AI approach ensures that each step is handled by the most appropriate tool or agent, avoiding the pitfalls of relying solely on an LLM for complex, multi-step processes. The result is a more reliable, accurate analysis that leads to actionable insights based on solid data.

Using Agentic AI ensures the extraction of high-quality insights. If you're interested in running a trial with your data, feel free to contact me on LinkedIn.

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sandsiv+ specializes in customer experience management, focusing on understanding and replicating human reasoning processes. We use AI to accurately reproduce, enhance, and improve how businesses engage with their customers. Our methodical approach ensures that our solutions are precise, reliable, and tailored to optimize customer interactions—because we believe there's no room for guesswork when it comes to keeping customers happy.