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GPT-4 is Here: What Changes for Enterprise AI

AI OpenAI GPT-4 Azure Machine Learning

The revelation that landed with GPT-4’s launch wasn’t just the capability improvement—it was learning that Microsoft’s new Bing Chat had been running on GPT-4 since Bing Chat launched in February 2023. The Bing Chat / GPT-4 confirmation immediately reframed everything Bing Chat had demonstrated publicly: the multi-step web research capability, the coherent long-form answer synthesis from multiple search results, the conversational follow-up handling—those weren’t just the Sydney chatbot being creative, they were GPT-4 reasoning capabilities applied to web search grounding. What changes in production applications: the 8K context window (standard GPT-4) vs 4K (gpt-35-turbo) immediately relaxes the most painful context management constraint—more conversation history, longer retrieved documents, more complex system prompts, all become feasible without aggressive truncation. The reasoning quality improvement: the most impactful real-world difference I found after testing was in agentic workflows where the model needs to reason about tool selection and error recovery—GPT-4 is substantially better at recognising when a tool call failed and adjusting the approach, rather than repeating the same failed strategy or hallucinating a successful outcome.

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.

Resources

Michael John Peña

Michael John Peña

Senior Data Engineer based in Sydney. Writing about data, cloud, and technology.