Skill: Tesla Company Profile Q&A

Domain

automotive

Description

Answers questions about Tesla's business model, operations, customer journey, manufacturing, battery technology, and strategy based on the Bain & Company Tesla Company Profile document. Provides accurate citations with document name and page numbers.

Business Rules

This skill provides question-answering capabilities on the Tesla Company Profile PDF document:

1. •Citation Requirement: All answers must include citations in the format: [TeslaCompanyProfile.pdf, Page X]
2. •Multi-Page Answers: When information spans multiple pages, all relevant page numbers must be cited
3. •Content Scope: Answers are limited to information contained within the PDF document
4. •No Speculation: If information is not in the document, the skill should indicate this clearly

Document Coverage

The Tesla Company Profile (46 pages) covers:

• •Executive summary and mission (Page 1)
• •Master plan timeline and strategy (Page 2)
• •Organizational structure (Page 3)
• •Key financial numbers and market position (Page 4)
• •Battery technology and cost improvements (Pages 5-6)
• •Vehicle models and pricing (Page 7)
• •Customer journey overview (Page 8)
• •Showroom and retail strategy (Page 9)
• •Direct-to-consumer sales model (Pages 10-16)
• •Financing options (Page 17)
• •Over-the-Air (OTA) updates (Pages 18-20)
• •Owner account and mobile app (Page 21)
• •Service operations (Page 22)
• •SKU simplification (Page 23)
• •Operating model and manufacturing (Pages 24-33)
• •Incentives and state regulations (Pages 34-36)
• •Distribution network (Pages 37-38)
• •Trade-in process (Pages 39-41)
• •Sales tax handling (Page 42)
• •Competitor information (Pages 43-46)

Input Parameters

• •question (string): The question to answer about Tesla based on the PDF content

Output

Returns an answer with:

• •answer (string): The response to the question based on PDF content
• •citations (list): List of citations in format {"source": "TeslaCompanyProfile.pdf", "pages": [page_numbers]}
• •confidence (string): "high", "medium", or "low" based on how directly the content addresses the question

Usage Example

from tesla_qa import answer_question

result = answer_question(
    question="What is Tesla's mission statement?"
)

print(f"Answer: {result['answer']}")
print(f"Citations: {result['citations']}")

Tags

automotive, tesla, electric-vehicles, business-model, question-answering, document-qa

Implementation

The Q&A logic is implemented in tesla_qa.py and references content from:

• •pdf_content.csv - Page-by-page content from the PDF document
• •page_topics.csv - Topic index mapping pages to key subjects
• •metadata.csv - Document metadata

Test Execution

from tesla_qa import answer_question

# Call the skill function
result = answer_question(
    question=input_data.get('question')
)

# Format output
output = {
    'answer': result.get('answer'),
    'citations': result.get('citations'),
    'confidence': result.get('confidence')
}