Artificial intelligence, a restructuring technology, has introduced the industry with prompt engineering. While this is a new field, the results from the efforts have reduced time, resource, and money consumption. For instance, you can build a chatbot from scratch with prompt engineering faster. Instead of taking a longer time writing code and manually building AI models, the industry-wide businesses are opting for AI chatbot development with prompt engineers.
These skilled experts have the ability to take less time and build an entire chatbot with Generative AI models. In the place of hiring AI model developers with extensive charges, businesses save investment, get their model built, release it with ease in the market, and assess the feasibility of their chatbot in a much shorter time and money.
Since the pretext focuses on efficiency increment, decision makers are apprehensive about the possibilities of generative AI development services. Now that entrepreneurs are contemplating the possibility, it is preferred to understand the basics of how to create a chatbot with generative AI. That being said, our focus is to bring a step-by-step prompt engineering manual.
Guide on AI Chatbot Development With Prompt Engineering
Before beginning with the step-by-step process, we must always remember that while prompt engineering is accessible nowadays with the assistance of an AI model, experts can do it much faster. You must always consider getting the chatbot built by an AI chatbot development company and explaining to them that you need experts for prompt engineering. The assistance is always a cherry on top as they bring in insights worth contemplation that first-timers might not have considered.
Since the pretext is presented, let us begin with the systematic process you or an experienced AI developer must follow.
Understand Your Use Case
Prior to beginning with anything, you must be aware of the intricate details about the chatbot and must set the tone of how it will behave. What is the purpose of the chatbot? Who will interact with it? Are we focusing on a distinctive industry here? Everything must be clear before prompt engineering.
# Example intents format (HR bot use-case)
intents = {
"intents": [
{"tag": "leave_policy", "patterns": ["Can I carry forward unused leave?"], "responses": ["Unused leave can be moved forward for up to 15 days."]}
]
}Set Up Prompt Engineering Tool
Since we are using prompt engineering to build the chatbot, the fundamental need for an ML model is eliminated. You don’t need an ML model, but access to one and some integration-focused tools assisting in the future. When you choose Generative AI development services for prompt engineering, the majority of your tasks wind up with the steps and they build the chatbot without any delay on R&D.
# Install modern tools
pip install nltk tensorflow keras
# Load and tokenize data
from tensorflow.keras.preprocessing.text import Tokenizer
tokenizer = Tokenizer(oov_token="<OOV>")
tokenizer.fit_on_texts(sentences)Create a System Prompt
Stating the pretext that a prompt engineer has taken up the AI chatbot development project, the next task is to build a system prompt. Over here, the expert engineer provides the model with detailed instructions on how the chatbot will behave.
= "You are a friendly HR assistant. Answer only HR-related questions clearly and politely.You may ask how that can be done. To explain that, if you are building a human resources chatbot, you will give the system the prompt “ You are a friendly HR assistant who answers employee questions clearly and politely. Do not answer questions outside the HR domain.” This way, the generative AI model will restrict itself and start assessing the roles and responsibilities.
Add Few-Shot Examples
To set the tone and roles further, the prompt engineer begins by providing the Gen AI model with the few-shot examples that assist in the model learning the preferred responses. In chatbot prompt engineering, you must know them prior. To bring an example, look at the questions and answers mentioned below for reference.
= ["Can I carry forward unused leave?", "How many annual leaves do I get?"]
sample_answers = ["You can carry forward up to 15 days.", "You are entitled to 24 paid leaves annually."]| User Question (Q) | Chatbot Response (A) |
| Can I carry forward unused leave? | Unused leave can be moved forward for up to “n” days. |
| What documents can I submit for reimbursement? | Please upload your bills and receipts in the HR portal under the ‘Reimbursements’ tab. |
| How many leaves am I entitled to annually? | You are entitled to 24 annual paid leaves, excluding public holidays. |
| Whom should I contact for payroll-related issues? | Please contact the payroll team at xyz@company.com. |
Use Conditional Instructions
As the step implies, in this step of AI chatbot development with prompt engineering, the engineer applies conditional instructions to the chatbot. While the chatbot model was provided with possible answers, now begin the engineering focusing on restricting non-applicable responses. Some of the examples are:
“Only answer in English.”
“Avoid giving legal advice.”
“If unsure, ask the user to rephrase.”
“Choose only to respond with HR related answers.”
This way, the chatbot model will avoid responses that are not essential for the purpose it is working on.
# Logic in response generation
if "law" in user_input.lower():
print("I'm not authorized to provide legal advice.")
elif not user_input.isascii():
print("Please ask your question in English.")Testing Through Prompt Tuning
Now that the model is refined and is live in the testing environment, the prompt engineer and the testing team conduct quality assurance fine-tuning. In this step, they test with 20-30 real user queries, assess how the chatbot model handles tricky questions, and adjust the prompt accordingly to pinpoint the issues and rectify them. This way, the chatbot is aligned with the task, becomes user-focused, and is ready to be deployed.
= ["What is the leave policy?", "Who do I talk to about payroll?"]
for query in test_queries:
response = model.predict(pad_sequences(tokenizer.texts_to_sequences([query]), maxlen=20))
print("Bot:", response)Deploy the Chatbot
Once the chatbot is ready and approved on all ends, the prompt engineer will deploy the chatbot model to the website, app, and software solutions. This way, the chatbot is up for the work and can now assist the users.
# Deploy with Flask (example)
from flask import Flask, request, jsonify
app = Flask(__name__)
@app.route('/chat', methods=['POST'])
def chat():
user_msg = request.json['message']
seq = tokenizer.texts_to_sequences([user_msg])
padded = pad_sequences(seq, maxlen=20, padding='post')
prediction = model.predict(padded)
response = random.choice(response_bank[np.argmax(prediction)])
return jsonify({"reply": response})
# Run the server
app.run()Conclusion
As we wind up the bifurcation on the step-by-step process to build a chatbot with the generative AI, we understand that assistance at the hands of an AI chatbot development company is imperative. This way, you can have the chatbot at your fingertips in less time, money, and resources.