If you’re exploring Talkative AI for the first time or looking to get more out of your existing setup, this guide is here to help. It’s designed to give you a clear understanding of how Talkative AI actually works.
With so much hype around AI, it’s easy to assume it can do everything. But like any tool, it works best when it’s set up with care, used with intention, and paired with the right knowledge. This guide will help you understand the core concepts that shape how our AI behaves, what it can and can’t do, and how to set realistic expectations as you get started.
🔍 How Talkative AI Works
To deliver accurate and helpful responses, Talkative AI uses a method called Retrieval-Augmented Generation (RAG). This approach combines the power of your carefully written knowledge base with the intelligence of Large Language Models (LLMs).
Rather than relying on guesswork, Talkative AI searches your approved content for relevant information, then uses that content to craft natural, human-like responses.
1. What is Retrieval-Augmented Generation (RAG)?
Instead of letting the AI make things up on its own, it first retrieves relevant content from your AI Knowledge Base (KB) and then uses that information to generate a response.
When you add content to your Knowledge Base, Talkative breaks it down into smaller, digestible sections called chunks.
Each chunk is like a mini-card of information, it could be a paragraph, a bullet point list, or a short section of an article. When the AI gets a customer query, it looks for the most relevant chunks, not the whole knowledge base.
Clear structure = better chunking = better answers.
We pair this with leading large language models (LLMs) like GPT-4.1, Gemini, or Claude, depending on your configuration.
2. What is a Large Language Model (LLM)?
A Large Language Model (LLM) is a powerful AI system trained on huge volumes of text. Its job is to understand language and generate human-like responses based on the information it’s given.
Once the relevant chunks are retrieved from your Knowledge Base, the LLM takes over. It reads those chunks, applies any prompt instructions, and generates a helpful, natural-sounding reply.
Think of the LLM as the voice of your AI, it doesn’t know your business by default, but it’s very good at turning your knowledge into conversations.
3. What are Prompts & Why Do They Matter?
Prompts are instructions that guide how your AI responds, think of them as the “personality layer” or “house rules” for your AI assistant.
🛡️ Built-in Prompts: These are automatically included with every AI response to:
- Enforce safety rules (e.g. “Only answer if confident”).
- Prevent risky behaviour (e.g. “Don’t give out contact details or prices”).
- Maintain basic tone and structure.
🧩 Custom Prompts: You can add your own prompt instructions to personalise how the AI responds. They let you define your AI’s tone, behaviour, and even its limitations, helping it stay aligned with your brand and business needs.
4. Bringing it All Together
Here’s how it all works in the background every time your AI replies:
This process is designed to be accurate, consistent, and aligned with your content and your brand, but the quality of the output depends on:
- The clarity of your Knowledge Base.
- The strength of your prompt.
- The AI model you choose.
If those pieces are solid, you’ll get smart, helpful responses every time.
🤖 What AI Can (and Can’t) Do
AI can be a powerful tool for improving efficiency, response times, and customer satisfaction, but it’s not magic.
- Respond quickly and consistently using your approved knowledge.
- Help customers with common questions or tasks.
- Support agents with smart suggestions.
- Save your team time through automation.
- Understand or act exactly as a human would.
- Guarantee 100% accuracy, it sometimes “hallucinates”.
- Access data it hasn’t been provided.
- Replace expert judgment or sensitive conversations.
Why isn’t it 100% accurate?
AI uses language prediction, not logic, to form answers. Even with a high-quality knowledge base, there are times when:
- The AI misunderstands the customer’s intent.
- There’s no clear answer available in your knowledge base.
- The prompt isn’t specific enough to guide its tone or behaviour.
When this happens, the AI may “hallucinate”, a term that means it generates an answer that sounds correct but isn’t. This is why we recommend always:
- Reviewing responses during setup.
- Using fallback instructions (like suggesting the customer speak to an advisor).
- Testing regularly and refining your prompt + KB content.
Want to understand how to test your AI? How to Test Your AI Knowledge Base is a step-by-step guide on testing and refining your setup.
🧰 Overview of Talkative AI Features
Talkative AI includes a suite of powerful tools designed to support both customers and agents across channels. From real-time conversations to behind-the-scenes reporting, each feature plays a different role in enhancing your support experience.
Each feature can be turned on or off, configured to your region, and tailored to meet your data privacy and compliance requirements.
Here’s a breakdown of what each feature does:
Features | What they do |
AI Knowledge Base | Stores the approved content your AI uses to answer questions. |
Chat AI | Responds to customers in real time on web chat, SMS, or social, using your AI knowledge base. |
Email AI | Suggests or sends email replies generated from your AI knowledge base. |
Voice AI | Handles phone calls in real-time with an interactive voice bot. |
AI Agent Assist | Provides live suggestions to agents during chats, based on conversation context and your knowledge base. |
AI Agent Rephrase | Offers agents rewording options for clearer or more professional responses. |
AI Agent Training | Uses fictitious scenarios to help onboard or train agents with realistic AI-generated chats. |
AI Interaction Summaries | Creates quick internal summaries of conversations, saving time for reviews or reporting. |
AI Interaction Data Capture | Automatically extracts key fields (like name, issue type) from chat transcripts. |
AI Analytics Assistant | Lets you query customer trends and insights using natural language. |
AI Insights Report | Periodic reports that highlight patterns, pain points, and agent performance using conversation data. |
AI Sentiment Analysis | Analyses the emotional tone of interactions to flag frustration, satisfaction, or urgency |
Want to dive deeper into each feature? You can find detailed explanations and watch demos in our Talkative AI Features Overview guide.
🔐 Data Privacy & Model Choice
Talkative gives you control over your AI data handling. Key points:
- No customer data is used to train the AI.
- You can choose from a range of LLMs (GPT-4.1, Gemini, Claude, etc.).
- Data is hosted in regional AWS environments, including London, US, and Australia.
- Most features use zero-retention models or local LLAMA hosting.
- You can configure your AI setup to align with your internal policies.
Want to understand how Talkative AI handles data? For full details on storage, sub-processors, and legal compliance, head over to our AI Legal & Data section.
❓Frequently Asked AI Questions
Still have questions about how Talkative AI works? You’re not alone! Below are some of the most common questions we get from customers during setup and testing.
Can the AI learn from conversations over time?
No, AI does not automatically learn from conversations. It uses a fixed Knowledge Base (KB) and does not “train” on live data. You remain in control of what it knows.
Can the AI handle multiple languages?
Yes, depending on your prompt and the model selected, AI can respond in multiple languages. You can instruct it to always reply in the same language as the customer.
What happens if the AI can’t answer?
It will either say it’s unsure (if the prompt tells it to), escalate to a human, or trigger a fallback, depending on how you’ve configured the behaviour.
Does Talkative use customer data to train its AI models?
No. Talkative does not use your customer data to train third-party models. You remain in control of your data, and processing happens within defined regional boundaries.
Which AI model is best for me?
It depends on your needs and regional setup. Talkative supports several leading Large Language Models (LLMs), and each has its strengths:
- GPT-4.1 (OpenAI)
- Strong all-rounder for most industries
- Performs well with complex queries or long-form answers
- Hosted in the US, with a 30-day retention period by default
Best for: Conversational accuracy, creativity, and reasoning.
- Gemini (Google)
- Strong choice for e-commerce, travel, or FAQ-heavy use cases
- Hosted in multiple regions (London, US, Australia), mirroring Talkative’s regional setup
- 0-day data retention; good for customers with stricter privacy requirements
Best for: Factual accuracy and speed in structured responses.
- Claude (Anthropic)
- Known for its “harmlessness” training, making it well-suited to legal, healthcare, or financial services
- Hosted regionally through AWS Bedrock in the UK, US, or Australia
- 0-day retention and strong compliance posture
Best for: Sensitive conversations, regulated industries, and long-context tasks.
If your question isn’t answered here, contact your CSM directly, or email us at support@gettalkative.com.