Prompt Engineering for Chatbots

In today’s fast-paced digital world, chatbots are more than just automated helpers—they’re frontline ambassadors for brands and services. But how do you ensure your bot responds accurately, remains on-brand, and drives real engagement? The secret lies in prompt engineering. By crafting clear, context-rich prompts, you can steer AI conversations toward useful, engaging outcomes every time.

Imagine giving your chatbot a vague instruction like “Tell me about our product.” You might get a generic, off-topic reply—or worse, a confusing answer that frustrates users. Prompt engineering transforms that one-line request into a precise conversation starter. It supplies the AI with context, tone, and direction so responses are:

This level of control not only boosts user engagement but also reduces support costs and keeps users coming back for more.

Broad questions yield broad answers. Instead, specify details: • “Explain our cloud-hosting tiers to a small business owner.” • “Compare Plan A vs. Plan B in bullet points.”

This narrows the AI’s focus and speeds user comprehension.

Assign roles to your chatbot:

“You are a friendly tech advisor.” “Speak as if you’re solving an urgent issue.”

Role-playing helps the AI adopt a consistent tone, keeping interactions on-brand and human-like.

Treat prompts like mini experiments. Test multiple versions, then compare results:

Show, don’t just tell. For instance:

“Write a greeting under 20 words that feels energetic.” “Use bullet points to list three benefits.”

Constraints guide the AI to produce exactly what you need.

Over-constraining stifles the AI; too much freedom leads to off-track answers. Aim for a middle ground:

This harmony fuels both accuracy and engagement.

Visit LLM Prompt Engineering tips and tricks to learn more about these strategies with a live example and prompt execution

LLMs main behaviour is predicting texts based on patterns in their learning data, not on verified facts. LLMs are great storyteller but sometimes unreliable fact-chekcers. What does this mean for our Chatbot is sometimes it can respond with made up policies which sounds real but it's not and is the result of model hallucination. To control model response and prevent drifting we can add multiple guardrails for different conditions.

In this example we are going to use these 5 strategies to write effective prompt for our Refund Chatbot that answers to our E-commerce shop refund questions and requests.

The first and most important step in writing prompt is defining tasks for our Chatbot, this step means what the chatbot should and shouldn't do.

Before we provide constraint about our policy in the prompt we should classify user's intent. Lets say we have the following policy

Here are some questions users can ask:

User's can also ask unrelated or ambiguous questions, like How is the weather today ? Who won the US election ? What should be our strategy for those questions ? Here is where intent classification shines.

First step in writing system prompt for Chatbot is to Classifying. It can be classifying user intent, classifying product type (for example we don't have refund for digital products).

For our case lets classify user's intent in five different categories

Let's Give our model a Role and few-shots (a couple of examples) about user's intent:

Let's add a couple of guardrails for ambiguous, unrelated and unclear questions. Here is where the intent classification come to help us, depending on the intent type we can define how to respond to it and where to put the guardrail. our guardrails will be like If the user intent is...respond like....

Let's add the guardrail for unrelated questions like the following to our prompt

If the user intent is unrelated_question, politely respond:

"I'm here to assist with refund-related questions only."

Let's add the guardrail for ambiguous questions like the following to our prompt : If the user intent is ambiguous, respond by asking for more details to determine eligibility. For example, ask about the purchase date, product type, or if they have a receipt.

Let's add the guardrail for ambiguous questions like the following to our prompt : If the user Intent is unclear, respond by, I don't know about that, Please contact support@example.com

We provided all of our instructions in only one prompt to the model. The best approach is to create different prompts for classifying users' intent first and feed the response of that prompt to the next prompt, which is in charge of responding to the user's intent. We should also implement a product classifier prompt to instruct the model about digital and physical products. classifiers prompt output -> main prompt -> show the response to the user

We can also take advantage of implementing user agents to access users' profiles to follow up with existing refund requests, for example.

Next, let's discuss LLM models' settings that can impact our chatbot response. these are

Prompt parameters are settings that tell an AI model how to generate text. Think of them as dials on a soundboard—each one shapes the final output. By adjusting these, you can make your chatbot concise or verbose, safe or imaginative, predictable or surprising.

It caps the number of tokens (roughly words/fragments) the model can produce. Defining max_tokens is highly important to control costs and latency.

A float between 0 and 2 that controls randomness. • Low (0.2–1.0): Deterministic answers—ideal for facts. • High (1.0–2.0): Creative, varied text—great for brainstorming.

A float between 0 and 1. Keeps generating from the smallest set of tokens whose cumulative probability ≥ top_p. For example, top_p=0.5 yields only the top 50% probable next-words, ensuring relevance while allowing variety.

Implementing prompt engineering best practices is only half the battle. You’ll want to track performance:

• Response Accuracy Rate: Percentage of correct, on-topic replies • Completion Time: How quickly the AI returns a usable response • User Satisfaction Scores: Collect feedback via quick surveys • Engagement Metrics: Click-throughs, session duration, repeat visits

Combine these metrics to refine prompts continuously. A quarterly review cycle ensures your chatbot evolves alongside user expectations.

Prompt engineering is both an art and a science. By applying these strategies—being specific, iterating, and measuring—you’ll transform your chatbot into an expert conversational partner.

Ready to dive deeper? Check out our article on the Definitive guide on prompt engineering. Let’s make every conversation count! 🎯