README.md

metadata

title: EduBot
emoji: 📚
colorFrom: yellow
colorTo: yellow
sdk: gradio
sdk_version: 5.42.0
app_file: app.py
pinned: false
short_description: Advanced prompt engineering for educational AI systems.

EduBot: Educational AI Assistant

Advanced Prompt Engineering Portfolio Project

Project Overview

EduBot demonstrates sophisticated prompt engineering techniques applied to educational technology, showcasing the implementation of context-aware AI systems that prioritize pedagogical effectiveness over simple answer generation. This project exemplifies professional-grade prompt design for educational applications with built-in academic integrity safeguards.

Technical Architecture

Core Technologies:

• LangChain: Prompt template management and conversation chain orchestration
• Gradio: Full-stack web interface with custom CSS styling
• Hugging Face Inference API: Model deployment and response generation
• Python: Backend logic and integration layer

Key Frameworks:

• langchain.prompts.ChatPromptTemplate for dynamic prompt construction
• langchain_huggingface.HuggingFaceEndpoint for model interface
• langchain.schema message objects (HumanMessage, AIMessage, SystemMessage)

Prompt Engineering Techniques Demonstrated

1. Context-Aware Template Selection

Implemented intelligent subject detection algorithm using keyword analysis to dynamically select appropriate prompt templates:

def detect_subject(message):
    # Keyword-based classification system
    # Routes to specialized educational templates

2. Role-Based System Prompting

Four distinct prompt templates employing specific pedagogical roles:

• Mathematics Tutor Template: Emphasizes conceptual breakdown and process explanation
• Research Skills Mentor Template: Focuses on source evaluation and methodology guidance
• Study Skills Coach Template: Incorporates learning style optimization and retention strategies
• General Educational Assistant Template: Comprehensive academic support framework

3. Instructional Design Integration

Each template incorporates evidence-based instructional design principles:

• Scaffolding: Breaking complex concepts into manageable components
• Socratic Method: Guiding discovery rather than providing direct answers
• Metacognitive Strategies: Teaching learning-how-to-learn approaches

4. Academic Integrity Constraints

Implemented ethical AI guidelines through prompt engineering:

• Explicit instructions to avoid homework completion
• Focus on process over product delivery
• Critical thinking skill development emphasis

Advanced Implementation Features

Conversation Memory Management

# LangChain message history integration
messages = [SystemMessage, HumanMessage, AIMessage]
# Maintains educational context across interactions

Response Streaming & Truncation

• Smart text truncation preserving sentence integrity
• Real-time response streaming for improved UX
• Error handling and fallback mechanisms

Template Chaining Architecture

chain = template | llm
response = chain.invoke({
    "question": message,
    "system_message": educational_context
})

User Interface Engineering

CSS Grid System Implementation

• Viewport-based height allocation (15% title, 60% chat, 25% input)
• Full-width responsive design
• Cross-browser compatibility optimization

Component Architecture

• Modular Gradio component structure
• Custom CSS class integration
• Accessibility-compliant design patterns

Prompt Engineering Methodologies Applied

1. Template Parameterization: Dynamic variable injection for contextual responses
2. Behavioral Constraint Definition: Explicit instruction sets for ethical AI behavior
3. Domain-Specific Language Modeling: Educational vocabulary and pedagogical terminology integration
4. Multi-Modal Response Formatting: Structured output generation with educational formatting

Professional Applications

This project demonstrates competency in:

• Enterprise-Grade Prompt Design: Scalable template architecture
• Educational Technology Integration: Pedagogically-informed AI system design
• Ethical AI Implementation: Academic integrity safeguards and responsible AI practices
• Full-Stack AI Application Development: End-to-end system implementation

Technical Specifications

Dependencies:

• langchain - Prompt orchestration and conversation management
• langchain-huggingface - Model integration layer
• gradio - Web application framework
• huggingface_hub - Model deployment interface

Deployment:

• Hugging Face Spaces compatible
• Environment variable configuration for API keys
• Production-ready error handling and logging

Results & Impact

EduBot represents a synthesis of prompt engineering best practices with educational technology requirements, demonstrating the ability to create AI systems that enhance rather than replace human learning processes. The project showcases advanced technical implementation while maintaining focus on pedagogical effectiveness and academic integrity.

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Portfolio Demonstration: This project evidences advanced prompt engineering capabilities, full-stack AI application development, and domain-specific AI system design suitable for enterprise educational technology environments.