Chatbots have become ubiquitous in enhancing customer experience (CX) for all kinds of businesses. Traditional rule-based chatbots were common before the year 2022 to provide 24/7 customer support. But, it falls short of delivering truly human-like responses since it runs on a rule-based system and canned responses.
Later, the arrival of Conversational AI, a type of artificial intelligence, promises more natural, dynamic, human-like conversation. Yet even these advanced chatbots can struggle with robotic tone, knowledge gaps, and emotional intelligence, leaving customers unsatisfied.
The impact?
The CGS survey conducted among 1000 US consumers in 2019 revealed that 86% of them prefer to interact with a human agent!
The good news?
Let me show you how you can eliminate the knowledge gap and the lack of human-like conversation of chatbots.
Why should chatbots sound like humans?
To create a smoother and personalized experience for your customers, the chatbot you choose must sound human. Otherwise, your conversion and retention rates will take a toll.
Having a chatbot that doesn’t sound like a human can lead to:
Poor customer engagement and satisfaction
- Frustrating interactions: Customers dislike repetitive, scripted responses that don’t address their needs naturally.
- Low resolution rates: Inflexible chatbots fail to understand nuanced queries, leading to unresolved issues until the human agent is back.
- Higher abandonment: Customers quickly exit chats if they feel like talking to a machine.
Negative brand perception
- Impersonal brand image: A robotic tone makes a company seem cold and unapproachable.
- Perceived as low-tech: While the competitors are investing in state-of-the-art technology, your brand will be remembered for being outdated and clunky.
Increased customer support costs
- More escalations to live agents: If the bot can’t handle simple queries well, humans must step in more often. It defeats the purpose of implementing automation.
- Longer resolution time: Users struggle to phrase questions in ways the chatbot understands, slowing down service.
Lower conversion and sales performance
- Ineffective upselling: Robotic, scripted recommendations feel pushy rather than helpful.
- Cart abandonment: Users drop off if the chatbot can’t answer purchase-related questions smoothly.
- No personalization: Non-human-like chatbots fail to respond based on user experience.
5 Things that make your AI chatbot more human
Before choosing a chatbot for your business, consider the following five things to eliminate the robotic tone and knowledge gap.
Natural language processing (NLP)
The chatbot you choose must support natural language processing (NLP) rather than just traditional rule-based chatbots. This is the first and foremost step of making your chatbot human.
NLP is the latest technology that enables machines to comprehend and understand human languages. Chatbots powered by NLP have an edge in analyzing sentiments in customer queries and segments based on positive, negative, and neutral.
A relatable example of NLP is Google Translate, which uses natural language processing techniques to translate text between multiple languages.
Model fine-tuning
Model fine-tuning is a process in which a pre-trained model is taken and trained on a new, typically smaller, dataset specific to a particular task or domain. This process adjusts the model’s internal weights and parameters to better suit the new data.
The goal of this process is to adapt the model’s existing knowledge to perform better on a specific task, such as customer service or technical support, improve its understanding of a particular domain, or adapt a desired style or persona.
If the fine-tuning data includes examples of incorrect chatbot response and their human-provided corrections, the model can learn to avoid those mistakes in the future, leading to more accurate and human-like interactions.
Just like the NLP processing, the fine-tuning can’t be done from your end. You should check with your chatbot vendor about this.
Better Prompts
Prompt engineering is one of the most underrated skills that can make a chatbot behave in a more humanly possible way.
A clear and well-structured prompt always makes it easy for LLMs that power chatbots. Based on OpenAI’s GPT-4.1 prompting guide, placing your instructions at the start and end of your prompt can improve results.
Let me show you how you can structure your prompts and place the instructions.
Prompt structure for a better human tone:
- Define the role and objective of your chatbot
- Instructions
- Output format
- Final instructions
Here’s a prompt for a customer support chatbot:
You’re a friendly and helpful customer support chatbot for [Your Brand]. Your goal is to assist users with questions, orders, or issues, making them feel heard, valued, and supported.
Instructions:
- Greet users warmly and introduce yourself.
- Keep your tone casual, human, and empathetic.
- Ask clarifying questions if needed.
- Give clear, step-by-step solutions.
- Personalize replies with user details when available.
- Reassure the user if you can’t solve something and offer next steps.
- Use light emojis only when it adds friendliness (e.g., 🙂👍).
Output Format:
- Friendly greeting
- Acknowledge their question or issue
- Provide a clear response or ask for more info
- Close with a helpful, kind message and offer further help
Final Instructions:
Sound like a helpful human, not a script. Be calm, clear, and kind in every interaction.
RAG Support
Even the most advanced AI models can leave knowledge gaps, which may cause chatbots to generate inaccurate or hallucinated responses. Addressing these gaps is key to making chatbot interactions more natural.
Unlike the NLP support and fine-tuning, providing some external knowledge can be done by yourself.
There is a method called retrieval augmented generation (RAG) that helps chatbots to provide up-to-date and contextually relevant information to improve the accuracy and factual grounding of their responses.
Let’s take a look at how RAG works.
The RAG process involves:
- Retrieval: Taking the user’s query and searching an external knowledge base (e.g., documents, databases, web pages) for relevant information
- Augmentation: Combining the retrieved information with the original user query into a prompt.
- Generation: Feeding this augmented prompt to the large language model (LLM), which then uses both its pre-existing knowledge and the newly provided context to generate a more informed and accurate response.
A good example of RAG implementation in a chatbot is connecting your knowledge base or technical documentation to it.
Feedback looping
The feedback looping is a great way to refine the chatbot’s language, conversational abilities, and overall behavior to become more aligned with human expectations and preferences.
The process of feedback looping is collecting feedback on the chatbots’ performance after it has been deployed and are interacting with users or the real world. This feedback can come in various forms, such as explicit ratings, user corrections, or even human evaluations of generated outputs.
ChatGPT showing two different answers to the same question and asking for your preference is a perfect example of feedback in action.
TL;DR
Conversational intelligence, natural language processing, model fine-tuning, better prompts, RAG support, and feedback looping are the essential technologies that make any chatbot sound like a human.
Businesses investing in natural language processing (NLP) supported chatbots like DeepAgent gain happier customers, stronger branding, and measurable ROI.
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