AI & Chatbot Development
In the AI & Chatbot Development internship program, you will explore the world of conversational AI by building chatbots and experimenting with popular tools like ChatGPT, Dialogflow, and Python libraries. This internship will guide you through key concepts in prompt engineering, rule-based systems, natural language processing, voice recognition, and large language model integration. You’ll gain practical experience by building real-world chatbot applications, from simple interfaces to context-aware bots using APIs and machine learning techniques.
Level 1: Easy Projects
Task 1: Exploring AI Tools like ChatGPT or Claude
Problem Statement:
Use a public LLM-based chatbot and test basic prompts.
Steps to Complete:
• Try different prompt formats
• Compare answers from ChatGPT, Bard, etc.
• Document prompt-response patterns
• Share results
Tools: ChatGPT, Claude, Bard, Google Gemini
Task 2: Creating Rule-Based Chatbot with Dialogflow
Problem Statement:
Build a basic chatbot with predefined intents and responses.
Steps to Complete:
• Sign up for Dialogflow or IBM Watson
• Define intents and responses
• Test using the web UI
• Deploy to a sample website
Tools: Dialogflow, IBM Watson, Google Cloud
Task 3: Designing Chat Flow with Flowchart
Problem Statement:
Map out a chatbot conversation using a flowchart tool.
Steps to Complete:
• Use tools like Whimsical or Draw.io
• Design 3–4 interaction paths
• Include greetings, questions, fallback messages
• Export the chart as PDF or image
Tools: Whimsical, Draw.io, Lucidchart
Level 2: Intermediate Projects
Task 4: Building a FAQ Bot with Python and Flask
Problem Statement:
Create a simple Python bot that returns FAQ answers.
Steps to Complete:
• Use Flask to build an API
• Create a JSON file for Q&A pairs
• Implement logic to match and return answers
• Host locally or on Replit
Tools: Python, Flask, JSON, Replit
Task 5: Integrating NLP for Text Classification
Problem Statement:
Use NLP to classify text as positive, negative, or neutral.
Steps to Complete:
• Use scikit-learn or Hugging Face transformers
• Train on a sentiment analysis dataset
• Build a simple prediction API
• Deploy locally
Tools: scikit-learn, Hugging Face, Pandas
Task 6: Voice Chatbot with Speech Recognition
Problem Statement:
Create a voice-enabled chatbot using speech-to-text and text-to-speech.
Steps to Complete:
• Use Python with speech_recognition and pyttsx3
• Capture voice input and convert it to text
• Respond using predefined logic and convert back to speech
• Record demo video
Tools: Python, speech_recognition, pyttsx3
Level 3: Advanced Projects
Task 7: Chatbot with GPT API Integration
Problem Statement:
Build a chatbot using OpenAI API and deploy it to a webpage.
Steps to Complete:
• Get an OpenAI API key
• Build a chatbot frontend (HTML/CSS/JS)
• Send requests to the GPT model and display answers
• Deploy on Netlify or GitHub Pages
Tools: OpenAI API, HTML/CSS/JS, Netlify
Task 8: Building a Context-Aware Chatbot
Problem Statement:
Develop a chatbot that can remember context across messages.
Steps to Complete:
• Use session-based architecture
• Store conversation history and use it in responses
• Implement context window with Python or Node.js
• Test for continuity
Tools: Python, Node.js, Flask
Task 9: AI Chatbot for Customer Support
Problem Statement:
Build a domain-specific chatbot with feedback loop and FAQs.
Steps to Complete:
• Choose a domain (e.g., e-commerce, banking)
• Define intents and conversation flows
• Train and deploy using Rasa or Dialogflow
• Allow users to rate responses and use for future training
Tools: Rasa, Dialogflow, Python, Google Cloud