Definition: A Large Language Model (LLM) is an AI system trained on billions of text documents to understand and generate human language with remarkable accuracy.
Large Language Models are the foundational AI systems that power modern chatbots, writing assistants, and AI search tools. When you interact with an AI chatbot that seems to understand you naturally — that's an LLM at work.
The most well-known LLMs include GPT-4 and GPT-4o (from OpenAI), Claude (from Anthropic), and Gemini (from Google). These models were trained on enormous datasets — trillions of tokens of text scraped from books, websites, code repositories, and more — giving them a broad understanding of language and knowledge.
What makes LLMs powerful is their ability to understand context. They don't just match keywords — they understand the meaning and intent behind a message, can hold multi-turn conversations, follow complex instructions, and generate coherent, relevant responses.
Converso lets you choose which LLM powers your chatbot and combines it with your specific content via RAG, ensuring responses are accurate and grounded in your knowledge base rather than relying on potentially outdated general training data.
LLMs are first trained on enormous text datasets — billions to trillions of words. During this phase, the model learns grammar, facts, reasoning patterns, and how language is used.
The base model is then fine-tuned with human feedback to be helpful, harmless, and honest. This is called Reinforcement Learning from Human Feedback (RLHF).
When you ask a question, the LLM predicts the most likely next tokens (words) given your input and the conversation context, generating a coherent response.
LLMs enable chatbots to hold conversations that feel genuinely human — not scripted, not robotic.
LLMs remember what was said earlier in a conversation and respond coherently across multiple turns.
Rather than matching keywords to pre-written answers, LLMs generate tailored responses to each specific question.
LLMs can synthesize information from multiple sources to answer complex, multi-part questions accurately.