March 18, 2023 5 min read

GPT-4 is Here: What Changes for Enterprise AI

OpenAI released GPT-4 this week, and within hours we learned that Bing Chat has been running on it since launch. This is a substantial leap forward, and I’ve been testing it to understand what it means for the applications we’re building.

What’s Different About GPT-4

Multimodal Capabilities

GPT-4 can process both text and images. This isn’t just OCR - it understands visual content:

import openai

response = openai.ChatCompletion.create(
    model="gpt-4-vision-preview",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Analyze this architecture diagram and identify potential bottlenecks."},
                {"type": "image_url", "image_url": {"url": "https://..."}}
            ]
        }
    ]
)

For data and analytics professionals, this opens up:

Analyzing charts and dashboards
Interpreting architecture diagrams
Understanding whiteboard sketches
Processing scanned documents

Significantly Better Reasoning

OpenAI tested GPT-4 on professional exams:

Bar Exam: 90th percentile (GPT-3.5: 10th percentile)
GRE Quantitative: 80th percentile
AP Calculus BC: 43rd percentile (GPT-3.5: failed)

This translates to better performance on complex tasks like:

Multi-step data analysis
SQL query optimization
Architecture decision reasoning
Debugging complex code

Longer Context Window

GPT-4 supports 8K tokens standard, with a 32K token version available. For context:

8K tokens ≈ 6,000 words
32K tokens ≈ 25,000 words

This means you can include much more context in your prompts - entire documents, long codebases, or extensive conversation histories.

# With GPT-4 32K, you can analyze entire files
with open('large_codebase.py', 'r') as f:
    code = f.read()  # Up to ~50 pages of code

response = openai.ChatCompletion.create(
    model="gpt-4-32k",
    messages=[
        {"role": "system", "content": "You are a senior code reviewer."},
        {"role": "user", "content": f"Review this code for performance issues and security vulnerabilities:\n\n{code}"}
    ]
)

Practical Implications for Data Platforms

Better SQL Generation

GPT-4’s improved reasoning shows immediately in SQL generation:

prompt = """Given these tables:
- orders (order_id, customer_id, order_date, total_amount, status)
- customers (customer_id, name, segment, created_date)
- order_items (item_id, order_id, product_id, quantity, unit_price)
- products (product_id, name, category, supplier_id)

Write an optimized SQL query to find the top 10 customers by total spend,
including only completed orders from the last 90 days, with their most
frequently purchased product category.
"""

# GPT-4 generates correct window functions, CTEs, and handles the edge cases

GPT-4 consistently handles complex joins, window functions, and edge cases better than GPT-3.5.

Architecture Analysis

I tested GPT-4’s ability to analyze data architectures:

architecture_description = """
Our data platform:
- Sources: 50 APIs, 3 databases, event streams from Kafka
- Ingestion: Azure Data Factory copying to ADLS Gen2 raw zone
- Processing: Databricks for transformations, writing to curated zone
- Serving: Synapse dedicated pool for reporting, Cosmos DB for applications
- BI: Power BI datasets connected to Synapse

Issues:
- Nightly jobs take 8 hours, often fail
- Analysts complain about stale data
- Costs are growing 20% monthly
"""

prompt = f"Analyze this architecture and provide specific, actionable recommendations:\n\n{architecture_description}"

GPT-4’s response identified specific bottlenecks and provided concrete recommendations, including questioning whether the dedicated pool was necessary (it suggested Synapse Serverless for the reporting workload).

Document Understanding

With image input, GPT-4 can analyze:

Data lineage diagrams
ERD diagrams
Power BI reports
Architecture drawings

This is valuable for documentation review and understanding legacy systems.

The Azure Connection

Microsoft confirmed GPT-4 will be available in Azure OpenAI Service, though timeline wasn’t specified. Current Azure OpenAI customers should expect:

Same API patterns, just new model deployment options
Higher pricing tier (GPT-4 is ~30x more expensive than GPT-3.5)
Potentially longer wait times during initial availability

What I’m Changing in My Approach

1. Moving Complex Analysis to GPT-4

For tasks requiring multi-step reasoning, GPT-4 is worth the cost premium. I’m now routing:

Complex SQL optimization
Code review tasks
Architecture analysis
Root cause analysis

2. Leveraging the Context Window

With 32K tokens, I can include:

Full stored procedure definitions
Complete configuration files
Entire conversation histories for long-running analyses

3. Exploring Vision Capabilities

Initial experiments with diagram analysis are promising. Use cases I’m exploring:

Automated architecture documentation
Dashboard analysis and optimization suggestions
Converting whiteboard designs to technical specs

Cost Considerations

GPT-4 is significantly more expensive:

GPT-3.5 Turbo: $0.002 / 1K tokens
GPT-4 (8K): $0.03 / 1K input, $0.06 / 1K output
GPT-4 (32K): $0.06 / 1K input, $0.12 / 1K output

For high-volume applications, this matters. Strategy:

Use GPT-3.5 for simple tasks (summarization, classification)
Route complex reasoning to GPT-4
Implement caching where possible
Monitor and optimize prompt efficiency

def select_model(task_complexity: str, requires_vision: bool) -> str:
    if requires_vision:
        return "gpt-4-vision-preview"
    elif task_complexity == "high":
        return "gpt-4"
    else:
        return "gpt-3.5-turbo"

Limitations Still Present

Hallucination: GPT-4 still makes things up, just less frequently. RAG patterns remain essential.

Knowledge Cutoff: Training data ends September 2021. Current Azure features and APIs still require documentation lookup.

Speed: GPT-4 is slower than GPT-3.5. For interactive applications, consider streaming responses.

The Bigger Picture

GPT-4 represents a capability threshold crossing. Tasks that were unreliable with GPT-3.5 are now viable:

Reliable complex code generation
Nuanced document analysis
Multi-step problem solving

The pace of improvement is remarkable. GPT-4 in many ways exceeds what I expected AI to achieve by 2025. For those of us building with these tools, the applications we can create are expanding rapidly.

Stay curious. Keep experimenting.