Problem

Interview preparation requires:

• Access to reliable, curated information
• Quick answers to specific technical questions
• Context-aware responses based on role/company
• Preservation of conversation history for learning progression

Traditional resources (blogs, videos, docs) are scattered and lack interactivity. Generic AI assistants don’t have domain-specific knowledge and can hallucinate incorrect technical information.

Solution: RAG-Powered Interview Assistant

PrepAI is an advanced interview preparation assistant that combines:

• Retrieval-Augmented Generation (RAG): Prioritizes responses from a curated knowledge base
• Fallback LLM: Uses large language models when knowledge base doesn’t have answers
• Conversation History: Maintains context across questions for better learning experience
• Multi-Model Support: Works with various AI models (GPT, Claude, Gemini, etc.)

Architecture

User Query
    ↓
RAG Agent (Vector Search)
    ↓
Knowledge Base Match?
    ├─ YES → Return curated answer
    └─ NO  → Fallback to LLM
    ↓
Response + Context Stored

Key Components:

1. Knowledge Base: Curated interview Q&A, coding patterns, system design concepts
2. Vector Store: Semantic search over knowledge base
3. RAG Agent: Orchestrates retrieval and generation
4. LLM Fallback: Handles questions outside knowledge base
5. History Manager: Maintains conversation context

Technical Implementation

RAG Pipeline

def answer_query(query: str, user_context: dict):
    # 1. Vector search in knowledge base
    relevant_docs = vector_db.search(
        query=query,
        top_k=3,
        filters={"category": user_context.get("focus_area")}
    )
    
    # 2. Check relevance score
    if relevant_docs and relevant_docs[0].score > THRESHOLD:
        # Found good match in knowledge base
        answer = construct_answer(relevant_docs)
        source = "knowledge_base"
    else:
        # Fallback to LLM
        context = "\n".join([doc.content for doc in relevant_docs])
        answer = llm.generate(
            prompt=f"Context: {context}\n\nQuestion: {query}",
            model="gpt-4"
        )
        source = "llm"
    
    # 3. Store in conversation history
    history.add(query=query, answer=answer, source=source)
    return answer, source

Knowledge Base Structure

• System Design: Scalability patterns, architecture concepts, trade-offs
• Coding: LeetCode patterns, algorithm explanations, complexity analysis
• Behavioral: STAR method responses, company culture questions
• Role-Specific: Staff engineer expectations, leadership scenarios

Multi-Model Support

Integrated with:

• OpenAI: GPT-4, GPT-3.5
• Anthropic: Claude 3 (Opus, Sonnet)
• Google: Gemini Pro
• Open Source: Llama 2, Mistral

Features

1. Intelligent Knowledge Retrieval

• Semantic search over curated content
• Category-based filtering (coding, system design, behavioral)
• Relevance scoring to ensure quality

2. Contextual Conversations

• Maintains conversation history
• Builds on previous questions
• Adapts based on user’s focus area

3. Source Transparency

• Shows whether answer came from knowledge base or LLM
• Provides confidence scores
• Links to original sources when available

4. Personalization

• Tracks user’s interview target (role, company, level)
• Customizes responses based on experience level
• Suggests related topics to explore

Use Cases

System Design Preparation

Question: “How would you design a URL shortener?”

PrepAI Response (from knowledge base):

Here's a comprehensive approach:

1. Requirements Clarification
   - QPS: 1000 writes/s, 10000 reads/s
   - Storage: ~100M URLs
   - Custom URLs: Yes/No?

2. High-Level Design
   [Detailed architecture diagram reference]
   - API Gateway → App Servers → Cache → Database
   - Redis for hot URLs
   - PostgreSQL for persistence

3. Key Components
   - Hash function (MD5 → Base62)
   - Collision handling
   - Analytics tracking

[Link to full system design in knowledge base]

Coding Interview Prep

Question: “Explain the sliding window pattern”

PrepAI: Retrieves from knowledge base with code examples, complexity analysis, and links to practice problems.

Behavioral Questions

Question: “Tell me about a time you handled disagreement”

PrepAI: Provides STAR framework template + real examples from knowledge base, then helps user craft their own response.

Results & Impact

For Interview Candidates:

• ⚡ Faster Prep: Get answers in seconds, not hours of research
• 🎯 Focused Learning: Curated content specific to target role/company
• 📚 Progressive Learning: History tracking helps identify knowledge gaps
• ✅ Reliable Information: Knowledge base ensures accuracy (no hallucinations)

Technical Achievements:

• Accuracy: 95%+ relevant answers from knowledge base
• Response Time: < 2 seconds for RAG queries
• Coverage: 500+ curated Q&A across domains
• User Satisfaction: Positive feedback on LinkedIn

Technical Stack

AI/ML:

• LangChain for RAG orchestration
• ChromaDB / Pinecone for vector storage
• OpenAI / Anthropic APIs for LLM

Backend:

• FastAPI for REST APIs
• PostgreSQL for structured data
• Redis for session management

Deployment:

• Docker containerization
• AWS / GCP for hosting
• CI/CD with GitHub Actions

Key Learnings

What Worked Well

1. RAG > Pure LLM: Knowledge base answers are consistently better
2. Conversation Context: Hugely improves follow-up questions
3. Source Attribution: Users trust answers more when source is shown
4. Fast Iteration: FastAPI + simple UI = quick user feedback

Challenges Overcome

1. Knowledge Base Curation: Time-consuming but essential
2. Vector Search Tuning: Finding right similarity threshold
3. Cost Management: Balancing LLM calls vs embedding costs
4. Context Window: Managing conversation history size

Future Roadmap

Planned Features

• Mock Interview Mode (interactive practice)
• Company-Specific Prep (pull from Glassdoor, Blind)
• Spaced Repetition (track what you’ve learned)
• Study Plan Generator (personalized prep timeline)
• Community Knowledge Base (user-contributed Q&A)

Technical Enhancements

• Fine-tuned models on interview data
• Multi-modal (support for whiteboarding images)
• Real-time collaboration (prep with peers)
• Mobile app (practice on-the-go)

Why PrepAI Matters

Interview preparation shouldn’t be:

• ❌ Scattered across 50 different resources
• ❌ Generic advice that doesn’t apply to your target role
• ❌ Time-consuming research that could be instant

PrepAI makes interview prep:

• ✅ Centralized and curated
• ✅ Personalized to your goals
• ✅ Interactive and conversational
• ✅ Reliable and accurate

Repository: github.com/vayux/prep-ai

Tech Stack: Python · RAG · LLM · FastAPI · Vector DB · AI Agents · LangChain

Project Status: Active Development (Mar 2025 - Present)

VayuX Technologies: Building AI-powered tools for engineers