AI in Customer Support — Support and AI

AI in Customer Support: Chatbots, Classification/Routing, and RAG

Key applications include Auto-Replies/Knowledge Bases and Retrieval Augmented Generation (RAG).

Classification and routing of inquiries are also central to the system.

Key Metrics: AHT, FCR, NPS, CSAT

AI in customer support leverages RAG assistants, auto-replies, and routing systems.

Reduce Average Handling Time (AHT) and improve First Call Resolution (FCR)/Net Promoter Score (NPS) through knowledge base searches, inquiry classification, and generative prompts for agents.

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Knowledge Base: Sources, Citation, Version Control

Classification/Routing: Multilingual support and Service Level Agreements (SLAs) are crucial.

Response templates and security policies ensure consistent and reliable information delivery.

Frequently asked questions

How is RAG better than a ‘clean’ LLM? Pulls?

RAG is better than a ‘clean’ LLM because it retrieves relevant sources, reduces hallucinations, and adds citations.

How to increase FCR? Update articles by top-intent, measure impact on FCR/NPS?

To improve FCR (First Click Rate), focus on updating content based on the most popular user intents and consistently monitor the effect of these changes on both FCR and Net Promoter Score.

What about multilingualism? Language models or translation with quality control?

When considering multilingual support, you can choose between using language models directly or employing a translation service with robust quality control measures to ensure accurate localization.

How to avoid toxic responses? Filter?

To prevent the generation of toxic responses, implement content filters and sentiment validation before sending any generated text.

▶ Try it live

Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Hash Function Avalanche Visualizer simulation