Prompt Engineering for Chatbots: Core Strategies & Best Practices

Prompt engineering matters for chatbots because it directly influences their ability to understand user intent, generate accurate and relevant responses, and provide a natural, engaging conversational experience. Well-crafted prompts help chatbots interpret ambiguous or incomplete user inputs by adding necessary context and instructions, which reduces misunderstandings and irrelevant outputs

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.

COSTAR is a fundamental method to write effective prompts. The COSTAR framework (also written as CO-STAR) is a structured, methodical approach to prompt engineering designed to help you create clear, effective prompts for large language models (LLMs). It breaks down prompt creation into six key elements, ensuring that every aspect of the prompt is carefully crafted to produce accurate, relevant, and well-formatted responses. Let's explain what COSTAR stands for and how to apply the COSTAR framework to write effective prompts.

To apply the CO-STAR framework to write prompts for chatbots, follow its six structured components to craft clear, targeted, and effective instructions that guide the LLM toward producing high-quality responses:

Context Provide background information or the scenario to set the stage for the model.

Objective Clearly define what you want the chatbot to do.

Style Specify the manner or voice in which the chatbot should respond, such as formal, casual, professional, or humorous.

Tone Set the emotional vibe or attitude of the response, which could be empathetic, enthusiastic, neutral, or serious, depending on the use case.

Audience Identify who the chatbot is addressing to tailor the language complexity and content appropriately. For example, experts, beginners, children, or general users.

Response Define the desired format or structure of the chatbot’s output, such as a list, paragraph, bullet points, or JSON for integration.

Here are some examples of using COSTAR in chatbot prompts

Benefits of using CO-STAR for chatbot prompts:

Assign a role to the model to link to related knowledge and style in its trained data.

Be as specific as possible about the task the chatbot should do

The Chain of Thought or Step by Step Reasoning enables chatbots to process complex queries more effectively by breaking down problems into logical, manageable steps rather than providing immediate, surface-level answers. This structured reasoning improves the chatbot’s ability to understand user intent, infer context, and generate accurate, relevant, and transparent responses beyond simple pattern matching

A few examples significantly enhance the chatbot’s ability to understand and perform tasks accurately with minimal training data. By providing a few illustrative examples within the prompt, few-shot prompting enables the chatbot to engage in in-context learning, which helps it recognize patterns and produce more relevant, context-aware, and customized responses

To enhance user experience, reduce ambiguity, and improve the readability of the model response, we can define the output format

Guardrails in chatbot prompt engineering are essential to:

We can provide some examples of how the model should respond to inappropriate queries, and in the response section, we provide instructions about the guardrails.

In this example, we are going to write an effective prompt for our Refund Chatbot that answers queries about our E-commerce shop's refund policy.

One of the most important components of the objective in this case is to classify the user's intent. The following is our refund policy

Here are some questions users can ask:

Users 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.

For our case, let's classify users' intent in five different categories

Let's define the intent classification task in the objective, provide some examples in context about different intents, and desired responses

Since this is a refund policy chatbot, our users can be frustrated or unhappy with a product, so we should define a proper style tone and audience to provide a good user experience

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.

In this article, we learned about the best practices that can be applied to Chatbot prompt engineering. We reviewed the COSTAR framework and how we can apply it to our Chatbot to provide accurate and high-quality answers to our end users.

Ready to dive deeper? Check out our article on the Prompt Engineering Best Practices and the Definitive guide on prompt engineering.