How chatbots are built: The tech wizard behind the AI curtain
Chatbots have evolved from basic auto-replies to sophisticated AI assistants that can hold conversations, complete tasks, and even crack the occasional joke (some better than others). But what’s really going on behind the scenes? Let’s break down the key components, how they work together, and what powers a chatbot’s AI-driven brain.
What are the key parts of a chatbot?
A chatbot isn’t just one piece of technology—it’s a stack of different AI-powered tools working together. Here’s what goes into building one:
🧠 Large Language Model (LLM) – The brain
Purpose: This is where the chatbot goes to sit and think and generate it’s responses.
How it works: Trained on massive datasets (meaning they were fed a sh*t ton of information), it uses that data to predict what words should come next in a conversation.
Example:
📝 User prompt: “Once upon a time, a brave astronaut set foot on Mars and discovered…”
🔍 How the LLM knows what response to generate:
It recognizes storytelling patterns and common sci-fi themes.
It predicts likely word sequences based on past data (e.g., “Mars” is often followed by “red dust” or “alien discovery”).
It understands context, so it continues in a logical and engaging way.
💬 AI response: “…a mysterious, glowing artifact buried beneath the red dust. As they reached out to touch it, their suit’s sensors began flashing with unknown signals.”
🤖 AI agent – The action-taker
Purpose: A chatbot isn’t useful if it only talks—an AI agent helps it do things and complete tasks.
How it works: AI agents can search the web, retrieve documents, interact with APIs, and even automate workflows.
Example:
📝 User prompt: “Hey, where’s my order #456789? It was supposed to arrive yesterday.”
🤖 How the AI agent processes it:
Understands intent – The AI recognizes this as a shipping inquiry and extracts the order number.
Retrieves data – It connects to the company’s order database and pulls real-time tracking info.
Generates a response – It retrieves real-time tracking data instead of guessing.
• It personalizes the response based on the user’s specific order.
• It provides a solution (tracking link + estimated delivery).
💬 AI response: “I found your order! It was delayed due to weather conditions but is now scheduled for delivery by 5 PM today. You can track it here: [tracking link]. Sorry for the delay!”
🗄️ Retrieval-Augmented Generation (RAG) – The fact-checker
Purpose: Ensures the chatbot has accurate, real-time information instead of just relying on old training data.
How it works: It retrieves fresh data from external sources before generating a response.
Example:
📝 User prompt: “What’s Tesla’s stock price right now?”
🤖 How the RAG-powered chatbot processes it:
Understands intent – Recognizes that the user is asking for real-time financial data.
Retrieves data – Instead of relying on outdated training data, the chatbot queries a stock market API (like Yahoo Finance or Bloomberg).
Generates a response – Using the retrieved stock price, the chatbot crafts a clear, informative reply:
🔍 How the AI knows what to say:
It fetches live stock prices from sources such as Bloomberg or MarketWatch instead of generating an outdated response based on old data.
It includes useful context (percentage change, closing price).
It provides a source link for verification.
💬 AI response: “As of now, Tesla’s stock price is $213.75, up 2.3% from yesterday’s close. Here’s a link to the latest stock chart: [Live Stock Data].”
🔗 APIs & integrations – The connectors
Purpose: Lets chatbots interact with other apps and databases.
How it works: APIs connect chatbots to Google Calendar, Slack, databases, e-commerce platforms, and more.
Example:
📝 User prompt: “Hey, can you book a table for two at Hooters tomorrow at 7 PM?”
🤖 How the AI-powered chatbot processes it:
Understands intent – Recognizes the user wants to make a reservation at a specific restaurant.
Retrieves availability – Connects to the restaurant’s booking system (via OpenTable, Resy, or a custom API) to check open tables.
Confirms & generates a response – Once availability is verified, the chatbot finalizes the booking and responds:
🔍 How the AI knows what to say:
It retrieves real-time availability from the restaurant’s system.
It confirms the booking before responding, ensuring accuracy.
It sends a confirmation message to provide reassurance.
💬 AI response: “You’re all set! I’ve booked a table for two at Hooters tomorrow at 7 PM. You’ll receive a confirmation email shortly. Let me know if you’d like to make any changes!”
🏗 Memory & context handling – The filing cabinet
Purpose: Helps chatbots remember past interactions and hold longer, more coherent conversations.
How it works: Memory stores user preferences, ongoing discussions, or past requests.
Example:
📝 User prompt: “I’m thinking about taking a vacation. Any recommendations?”
🤖 AI response (initial): “That sounds exciting! Are you looking for a beach getaway, a city adventure, or something in nature?”
📝 User follows up a week later: “A beach getaway! Oh, by the way, what’s the best time of year to go there?”
🤖 How the AI uses memory & context:
Remembers past messages – The chatbot recalls that the user was considering vacation spots.
Understands context – Recognizes that “there” refers to the destination previously discussed.
🔍 How the AI knows what to say:
It remembers the vacation topic instead of asking again.
It links the follow-up question to past responses, making the conversation feel natural.
It keeps the discussion relevant, so the user doesn’t have to repeat themselves.
💬 AI response (final): “You should check out Bali! The best time to visit is between April and October, when the weather is warm and dry. Let me know if you need help with flights or accommodations!”
🎙 Voice & speech processing – The talker & listener
Purpose: Allows chatbots to interact using spoken language instead of just text.
How it works: Uses speech-to-text (STT) to convert voice into text and text-to-speech (TTS) to read responses aloud.
Example:
📝 User prompt (spoken): “Hey Siri, what’s the weather like today?”
🤖 How the AI processes the voice input:
Speech-to-text (STT) – The AI converts spoken words into text, recognizing the phrase: “What’s the weather like today?”
Understands intent – It detects that this is a weather inquiry and retrieves live weather data from a weather API (such as weather.com).
Text-to-speech (TTS) – After generating the response, it converts the text back into speech so Siri can speak the answer out loud.
🔍 How the AI knows what to say:
It translates speech to text before processing the request.
It retrieves real-time weather data from an external source.
It uses text-to-speech (TTS) so the response sounds natural and conversational.
💬 AI response (spoken): “It’s currently 72°F and sunny with a light breeze. No rain expected today!”
So, how do these parts work together?
When you message a chatbot, a lot happens behind the scenes:
1️⃣ User prompt – You type (or say) "What’s the best laptop for gaming?"
2️⃣ Processing & understanding – The chatbot’s LLM analyzes your request and determines the intent.
3️⃣ Retrieving data – If needed, the bot pulls real-time specs and reviews using RAG.
4️⃣ Generating a response – The chatbot builds a well-structured answer based on its knowledge and retrieved data.
5️⃣ Response delivery – The chatbot responds in text, or if it’s voice-enabled, it speaks back to you.
Repeat the cycle, and voilà—you’ve got an AI-powered conversation!
Final Thoughts
Chatbots aren’t magic—they’re a blend of AI models, retrieval systems, and integrations working in harmony. As they get smarter, more capable, and more human-like, they’ll continue reshaping how we interact with technology.
