Technical Guide
How Do Chatbots Work?
Understanding chatbot technology doesn't require a computer science degree. Here's how AI chatbots understand questions and provide intelligent answers.
The Basic Flow: Question → Understanding → Response
When a user asks a question, the chatbot follows three main steps:
- Understand the Question: The chatbot uses natural language processing (NLP) to parse the user's message, identify intent, and extract key information.
- Search Knowledge Base: It searches through your documents, FAQs, and content to find relevant information that answers the question.
- Generate Response: Using advanced AI models, it formulates a natural, human-like answer based on the found information.
Natural Language Processing (NLP)
NLP allows chatbots to understand human language beyond simple keyword matching. When someone asks "What's your return policy?" or "How do I send something back?", the chatbot recognizes these as similar questions about returns—even though the words are different.
Example
Human: "Can I return this if I don't like it?"
Chatbot understands: User is asking about return policy
Chatbot responds: "Yes! You can return items within 30 days of purchase. Here's how..."
Embeddings and Semantic Search
Modern chatbots use embeddings—mathematical representations of text that capture meaning. Words and phrases with similar meanings are positioned close together in a high-dimensional space.
This allows chatbots to find relevant information even when the user's question doesn't use the exact same words as your documentation. Instead of keyword matching, chatbots perform semantic search—finding content that means the same thing.
How Semantic Search Works
- 1. Your documents are broken into chunks and converted into embeddings (vectors)
- 2. The user's question is also converted into an embedding
- 3. The chatbot finds document chunks whose embeddings are closest to the question's embedding
- 4. These relevant chunks are used to generate the response
RAG: Retrieval-Augmented Generation
Veritas AI uses RAG (Retrieval-Augmented Generation), a technique that combines two powerful approaches:
- Retrieval: Find the most relevant information from your documents
- Augmentation: Provide that information as context to the AI model
- Generation: The AI generates an answer based on your actual content, not generic responses
This ensures chatbots stay grounded in your specific knowledge rather than hallucinating or making up answers.
Knowledge Base and Document Processing
Your chatbot's intelligence comes from your knowledge base—the documents, FAQs, product catalogs, and internal content you upload to Veritas AI.
When you upload documents:
- Documents are processed and broken into semantic chunks
- Each chunk is analyzed and converted into embeddings
- These embeddings are stored in a vector database for fast retrieval
- The chatbot uses these embeddings to find relevant content when users ask questions
Continue Learning
Now that you understand how chatbots work, explore how they benefit businesses and improve customer experience.