1 min read
GPT-4 Enterprise Use Cases
I wrote “GPT-4 Enterprise Use Cases” to share practical, production-minded guidance on this topic.
1. Contract Analysis and Legal Review
GPT-4’s 32K context can process entire contracts:
class ContractAnalyzer:
"""Analyze legal contracts with GPT-4."""
def __init__(self, client):
self.client = client
async def full_contract_review(self, contract_text: str) -> dict:
"""Comprehensive contract analysis."""
prompt = """Analyze this contract comprehensively.
Provide analysis in these sections:
1. **Executive Summary**: 2-3 sentence overview
2. **Key Terms**:
- Parties involved
- Effective dates
- Termination conditions
- Payment terms
3. **Obligations**:
- Your obligations (as the signing party)
- Counterparty obligations
4. **Risk Assessment**:
- High risk clauses (indemnification, liability)
- Unusual or non-standard terms
- Missing standard protections
5. **Recommendations**:
- Clauses to negotiate
- Questions to clarify
- Suggested modifications
Contract:
{contract}"""
response = await self.client.chat_completion(
model="gpt-4-32k",
messages=[
{"role": "system", "content": "You are a corporate attorney reviewing contracts. Be thorough and flag any concerns."},
{"role": "user", "content": prompt.format(contract=contract_text)}
],
temperature=0.2
)
return {
"analysis": response.content,
"tokens_used": response.usage.total_tokens,
"model": "gpt-4-32k"
}
async def compare_to_template(
self,
contract: str,
template: str
) -> dict:
"""Compare contract against standard template."""
prompt = """Compare this contract against our standard template.
Identify:
1. Missing clauses from our template
2. Additional clauses not in template
3. Modifications to standard language
4. Deviations in key terms (pricing, liability caps, etc.)
Standard Template:
{template}
Contract Under Review:
{contract}"""
response = await self.client.chat_completion(
model="gpt-4-32k",
messages=[
{"role": "user", "content": prompt.format(
template=template,
contract=contract
)}
]
)
return response.content
2. Code Review and Architecture Analysis
class CodeReviewer:
"""Comprehensive code review with GPT-4."""
async def review_pull_request(
self,
diff: str,
context_files: list[str] = None
) -> dict:
"""Review a pull request."""
context = ""
if context_files:
context = "\n\nRelated files for context:\n" + "\n---\n".join(context_files)
prompt = f"""Review this pull request diff.
Provide feedback on:
1. **Correctness**: Logic errors, edge cases, potential bugs
2. **Security**: Vulnerabilities, injection risks, auth issues
3. **Performance**: Inefficiencies, N+1 queries, memory issues
4. **Maintainability**: Code clarity, naming, documentation
5. **Testing**: Missing test cases, edge cases to cover
For each issue, specify:
- Severity (Critical/High/Medium/Low)
- File and line number
- Description
- Suggested fix
Diff:
{diff}
{context}"""
response = await self.client.chat_completion(
model="gpt-4",
messages=[
{"role": "system", "content": "You are a senior software engineer conducting a thorough code review."},
{"role": "user", "content": prompt}
],
temperature=0.1
)
return self._parse_review(response.content)
def _parse_review(self, content: str) -> dict:
"""Parse review into structured format."""
# Implementation to extract issues, severity, etc.
return {"raw": content, "parsed": True}
async def analyze_architecture(
self,
codebase_summary: str,
specific_concern: str = None
) -> dict:
"""Analyze codebase architecture."""
prompt = f"""Analyze this codebase architecture.
{f'Specific concern to address: {specific_concern}' if specific_concern else ''}
Codebase Summary:
{codebase_summary}
Provide analysis on:
1. Overall architecture pattern (monolith, microservices, etc.)
2. Separation of concerns
3. Dependency management
4. Scalability considerations
5. Testing architecture
6. Potential improvements"""
response = await self.client.chat_completion(
model="gpt-4-32k",
messages=[{"role": "user", "content": prompt}]
)
return response.content
3. Financial Analysis and Reporting
class FinancialAnalyzer:
"""Financial document analysis with GPT-4."""
async def analyze_financial_statements(
self,
statements: str,
period: str = "Q4 2022"
) -> dict:
"""Analyze financial statements."""
prompt = f"""Analyze these financial statements for {period}.
Financial Statements:
{statements}
Provide:
1. **Key Metrics Summary**:
- Revenue and growth rate
- Gross margin
- Operating margin
- Net income
- Cash flow from operations
2. **Trend Analysis**:
- QoQ and YoY comparisons
- Concerning trends
- Positive developments
3. **Health Indicators**:
- Liquidity ratios
- Debt levels
- Working capital
4. **Risk Factors**:
- Identified concerns
- Areas needing investigation
5. **Outlook**:
- Based on current trends
- Key factors to watch"""
response = await self.client.chat_completion(
model="gpt-4-32k",
messages=[
{"role": "system", "content": "You are a financial analyst. Provide thorough, accurate analysis."},
{"role": "user", "content": prompt}
],
temperature=0.2
)
return response.content
async def earnings_call_summary(
self,
transcript: str
) -> dict:
"""Summarize earnings call transcript."""
prompt = f"""Summarize this earnings call transcript.
Transcript:
{transcript}
Provide:
1. **Key Announcements**: Major news and updates
2. **Financial Highlights**: Numbers mentioned
3. **Guidance**: Forward-looking statements
4. **Management Tone**: Optimistic/cautious/concerned
5. **Analyst Concerns**: Key questions and answers
6. **Red Flags**: Any concerning statements"""
response = await self.client.chat_completion(
model="gpt-4-32k",
messages=[{"role": "user", "content": prompt}]
)
return response.content
4. Customer Support Escalation Analysis
class SupportAnalyzer:
"""Analyze support escalations with GPT-4."""
async def analyze_escalation(
self,
ticket_history: str,
customer_profile: str = None
) -> dict:
"""Analyze support escalation for resolution."""
context = f"\nCustomer Profile:\n{customer_profile}" if customer_profile else ""
prompt = f"""Analyze this support escalation.
Ticket History:
{ticket_history}
{context}
Provide:
1. **Issue Summary**: What is the core problem?
2. **Root Cause Analysis**:
- Technical cause (if identifiable)
- Process failure (if applicable)
- Communication gaps
3. **Customer Sentiment**:
- Frustration level (1-10)
- Key pain points
- Expectations
4. **Resolution Recommendation**:
- Immediate actions
- Long-term fix
- Compensation recommendation (if warranted)
5. **Prevention**:
- How to prevent similar escalations
- Process improvements needed"""
response = await self.client.chat_completion(
model="gpt-4",
messages=[
{"role": "system", "content": "You are a customer success manager analyzing escalations to find optimal resolutions."},
{"role": "user", "content": prompt}
]
)
return response.content
async def batch_analyze_tickets(
self,
tickets: list[dict]
) -> dict:
"""Analyze patterns across multiple tickets."""
tickets_text = "\n---\n".join([
f"Ticket {t['id']}: {t['summary']}\nCategory: {t['category']}\nResolution: {t['resolution']}"
for t in tickets
])
prompt = f"""Analyze these support tickets for patterns.
Tickets:
{tickets_text}
Identify:
1. Common issues and their frequency
2. Categories needing attention
3. Resolution effectiveness
4. Systemic problems
5. Recommendations for improvement"""
response = await self.client.chat_completion(
model="gpt-4-32k",
messages=[{"role": "user", "content": prompt}]
)
return response.content
5. Technical Documentation Generation
class DocumentationGenerator:
"""Generate technical documentation with GPT-4."""
async def generate_api_docs(
self,
openapi_spec: str
) -> str:
"""Generate human-readable API documentation."""
prompt = f"""Generate comprehensive API documentation from this OpenAPI spec.
OpenAPI Spec:
{openapi_spec}
Generate documentation including:
1. Overview and authentication
2. Each endpoint with:
- Description
- Parameters
- Request examples
- Response examples
- Error codes
3. Common use cases
4. Rate limits and best practices
Format in Markdown."""
response = await self.client.chat_completion(
model="gpt-4-32k",
messages=[{"role": "user", "content": prompt}]
)
return response.content
async def generate_runbook(
self,
system_description: str,
incident_types: list[str]
) -> str:
"""Generate operational runbook."""
incidents = "\n".join(f"- {i}" for i in incident_types)
prompt = f"""Generate an operational runbook for this system.
System Description:
{system_description}
Incident Types to Cover:
{incidents}
For each incident type, include:
1. Detection criteria
2. Impact assessment steps
3. Step-by-step resolution
4. Escalation criteria
5. Post-incident actions
Format in Markdown with clear sections."""
response = await self.client.chat_completion(
model="gpt-4",
messages=[{"role": "user", "content": prompt}]
)
return response.content
ROI Considerations
| Use Case | Manual Time | GPT-4 Time | Cost Savings |
|---|---|---|---|
| Contract review | 4 hours | 10 min | ~$400/contract |
| Code review | 1 hour | 5 min | ~$100/PR |
| Financial analysis | 2 hours | 15 min | ~$200/report |
| Support escalation | 30 min | 2 min | ~$50/ticket |
GPT-4’s cost (~$0.10 per analysis) is negligible compared to time savings.
Implementation Strategy
- Start with high-value, low-risk: Documentation, analysis
- Add human review: AI assists, humans verify
- Measure quality: Track accuracy, corrections needed
- Expand gradually: Build confidence before automation
GPT-4 enables automation of knowledge work that was previously too complex for AI. The enterprise applications are substantial.\n\n## Takeaways\n\nAdd a concise, personal takeaway and recommended next steps here.\n