Large Language Model (LLM)
A Large Language Model (LLM) is a deep learning model trained on billions of tokens of text data. LLMs can understand context, follow instructions, and generate coherent text across a wide range of tasks. In customer support, LLMs power AI chatbots that can understand customer questions and generate helpful, natural-language responses.
LLMs in customer support
LLMs enable support chatbots to understand the intent behind customer questions — even when phrased informally or with typos. Unlike rule-based chatbots that match keywords to scripted responses, LLM-powered support can handle novel questions, maintain conversational context, and generate unique responses tailored to each query.
Limitations of standalone LLMs
A standalone LLM only knows what it was trained on — it has no knowledge of your specific product, pricing, or documentation. This is why support AI systems pair LLMs with retrieval systems (RAG) to ground responses in your actual content, rather than relying on the model's general training data.
EchoSDK's LLM integration
EchoSDK uses Google Gemini as its language model, paired with a RAG pipeline for document retrieval. The LLM receives your documentation as context with each query, ensuring responses are accurate and specific to your product — not generic AI-generated content.
Related terms
Retrieval-Augmented Generation (RAG)
An AI technique that combines a language model with a retrieval system to generate answers grounded in specific documents or data sources.
AI Hallucination
When an AI model generates a response that sounds plausible but is factually incorrect or fabricated, not grounded in actual data.
Natural Language Processing (NLP)
A branch of AI focused on enabling computers to understand, interpret, and generate human language in a meaningful way.