Know Your LLM (Series): ChatGPT

ChatGPT is widely used in enterprise AI, with OpenAI offering GPT-5, GPT-4.1 long-context models, o-series reasoning models, and real-time voice models like gpt-realtime. Because OpenAI does not publish the exact routing used inside ChatGPT plans, this article focuses on documented model capabilities and integration patterns rather than internal routing details.

Overview of the model

Model family and architecture class

ChatGPT uses multiple OpenAI model families rather than a single model. Key publicly documented families include:

GPT-5 (GPT-5, mini, nano): Advanced general-purpose models with strong reasoning; smaller variants trade cost for speed
GPT-4.1 (4.1, mini, nano): Strong instruction-following and coding performance with very large context windows
o-series (e.g., o3): Optimized for deep, multi-step reasoning
gpt-realtime: Low-latency models for voice and interactive streaming

All operate as decoder-only Transformer models with proprietary internals.

Parameter scale, context length, modality support

OpenAI differentiates models by capability and context window rather than parameter counts.

GPT-4.1: supports up to 1M tokens and performs strongly on coding tasks, making it ideal for long-context or large-code workloads
GPT-5: focuses on improved reasoning, control, and reliability, offering large context windows but OpenAI has not published a single standard context size across the entire family

ChatGPT models are multimodal, supporting natural language, structured outputs (JSON, SQL, YAML, etc.), strong code generation, vision for interpreting images and screenshots, audio via gpt-realtime, and image generation via separate models.

Native tool use and function calling

Modern OpenAI models act as tool-using agents rather than simple text generators. The API supports structured tool calling, where each tool includes a name, description, and JSON schema, and models decide when and how to call them. GPT-4.1 and GPT-5 are tuned for strong instruction following and reliable tool use. The model interprets the user's request, selects and sequences tool calls, generates SQL and transformations, and synthesizes the final answer.

Strengths and limitations for enterprise workloads

Strengths:

Higher reliability than GPT-4-class models, with improved reasoning and coding (GPT-5)
Long-context support via GPT-4.1
Strong coding and SQL generation across both model families

Limitations:

Hallucinations still occur; tool-first workflows and validation are required
Outputs are non-deterministic without tight control of temperature and formats
Large context windows increase latency and cost; targeted context is more efficient

Documentation and technical specifications to review

Official model/API documentation

Helpful references include OpenAI's model catalog and API reference, which describe capabilities, context windows, benchmarks, pricing, and availability. The function calling guide and structured outputs documentation explain how to define tools, when models choose to call them, and how to enforce structured outputs. Pricing details and rate limits provide information on token costs, long-context pricing, cache behaviour, and model-specific constraints.

Because OpenAI updates models and pricing frequently, treat any figures as accurate only as of their stated date, and verify against the latest official documentation at OpenAI Platform.

Rate limits and throughput

OpenAI communicates rate limits through the dashboard and HTTP headers such as x-ratelimit-limit-requests, x-ratelimit-remaining-tokens, and x-ratelimit-reset-requests. Limits vary by model, account tier, agreements, usage, and region.

Best practices include using backoff and retry logic, monitoring rate-limit headers, and routing simple tasks to lighter models while reserving heavier models for complex reasoning.

Authentication and security posture

Authentication uses project-scoped API keys or tokens that should be securely stored, regularly rotated, and scoped per environment. OpenAI's data-use policies state that API data is encrypted in transit and at rest, not used for training unless customers explicitly opt in, and may retain briefly (often up to 30 days) for service and abuse monitoring, with Zero Data Retention options available.

Integration pattern with CData Connect AI

Tools and MCP workflow

A typical ChatGPT + CData Connect AI flow includes a system prompt defining the agent's rules, MCP-style tool definitions, and a user query. The LLM then plans and calls tools checking available connections, discovering schemas, running SQL, and applying transformations as needed. Finally, it synthesizes results into a clear explanation, optional tables or JSON, and follow-up questions if needed. This workflow aligns with OpenAI's tool-calling design and CData's role as the secure data access layer.

Structured outputs and parsing

CData AI style systems produce two main output types:

Structured data for orchestration: JSON tool arguments, chart configs, and transformation specs
Natural-language responses for users: explanations, summaries, recommendations

Common practices include using JSON mode for tool calls, validating schemas before executing SQL or external actions, and keeping internal orchestration messages separate from user-facing content.

Error handling and self-correction

CData Connect AI tools may fail due to missing columns or tables, permission issues, timeouts, or resource limits. Within a tool-calling flow, the LLM can be prompted to interpret errors, re-check schemas, adjust SQL, retry a limited number of times, or explain issues to the user when they cannot be resolved. These behaviours rely on prompting and tool design, not built-in guarantees from OpenAI.

Understanding remote data access flows

The system prompt can make explicit that data comes from connectors (not the LLM), metrics and values must be derived from tool outputs, and the model must follow CData Connect AI's permission and governance rules. This ensures accurate, secure, and predictable behaviour.

Integration with other agents

ChatGPT models integrate effectively with multi-agent architectures through OpenAI's Assistants API and standard tool-calling interfaces. In CData Connect AI environments, ChatGPT coordinates with specialized agents for data validation, transformation logic, or domain-specific reasoning by maintaining conversation context and passing structured outputs via JSON. This enables hybrid workflows where ChatGPT handles natural language understanding and high-level planning, while delegating tasks like SQL optimization to database-specialized agents, data quality checks to validation agents, or complex calculations to numeric reasoning agents. Integration approaches include sequential orchestration, parallel execution, and hierarchical delegation with message-passing protocols and shared state management.

The key to successful multi-agent integration lies in clear agent boundaries, well-defined handoff protocols, maintaining shared context through conversation history, and monitoring capabilities that track performance across the agent ecosystem.

Evaluation criteria for CData Connect AI compatibility

SQL parsing and schema reasoning

When assessing ChatGPT for CData Connect AI, key questions include whether it maps natural-language queries to the correct tables and fields, reliably handles joins and aggregations, and adapts to different SQL dialects. GPT-4.1 and GPT-5 benchmarks indicate strong potential for natural-language-to-SQL generation, but real performance must be validated against the specific schemas and metrics in use.

Hallucinations over live data

LLMs operate in two modes:

Ungrounded: answers rely on training data and may invent values or schema details
Tool-grounded: mistakes usually stem from incorrect schema assumptions or business logic rather than fabricated numbers

GPT-5 improves reliability but does not offer a universal hallucination rate, so performance must be evaluated per Connect AI environment. Mitigation includes running schema discovery before SQL generation, using error messages to correct schema issues, and explaining answers with references to the tools and tables used.

Performance with CData's remote MCP execution

Connect AI workflows often span multiple connectors, require several tool calls, and may trigger expensive SQL or API operations. Evaluation focuses on whether the model selects a sensible tool sequence, handles errors gracefully, and keeps end-to-end latency acceptable. OpenAI's tool-calling provides the base capability, while actual success depends on tool design, prompts, and schemas.

Security and compliance considerations

Data retention

OpenAI's data-use policies state that API and enterprise data is not used for training unless customers opt in, may be retained briefly (often up to 30 days) for service and abuse monitoring, and can be covered by Zero Data Retention for eligible customers.

Encryption, access controls, and data residency

OpenAI's security documentation states that customer data is encrypted in transit and at rest, access is restricted and audited, and the platform is certified under SOC 2 Type II and ISO 27001. OpenAI offers data residency for eligible customers in selected regions such as the EU. Azure OpenAI Service offers comparable protections within Azure's compliance framework, though details may differ and should be verified in Microsoft's documentation.

Compliance

OpenAI states that its enterprise offerings comply with SOC 2 Type II and ISO 27001 and support GDPR through DPAs, data residency, and contractual terms. For regulated sectors like healthcare or government, OpenAI and Azure OpenAI can enable HIPAA-aligned deployments and BAAs where supported.

Benchmarking tasks against CData Connect AI

Multi-step analytics workflows

A typical CData Connect AI workflow identifies relevant data sources, discovers schemas, generates SQL or API calls, combines results through transformation tools, and summarizes the findings. ChatGPT's tool-calling and long-context features support this orchestration, though actual success and latency depend on the underlying tool design, prompts, schemas, and infrastructure.

Long and complex SQL generation

GPT-4.1 and GPT-5 handle complex SQL patterns including multi-CTE queries, window functions, and cross-source logic when CData AI provides a clear model. Their strong performance on code benchmarks suggests good SQL generation capability, though results must still be validated on the specific schemas and dialects in use.

Autonomous tool chaining

GPT-5's improved control over reasoning supports multi-step tool orchestration in CData Connect AI, helping guide tool chains, limit tool-call loops, and keep traces interpretable. Actual success rates for these workflows depend on the implementation and are not defined in OpenAI's public benchmarks.

Usability findings

Understanding of enterprise SaaS

Because of broad pretraining, the models understand common business metrics, analytics concepts, and the general data models of major SaaS platforms. This helps them map user language to likely tables or fields and propose reasonable filters. However, exact field names and business logic must always come from the real schemas.

Adapting to CData's 350+ sources

As CData normalizes APIs and databases into SQL-like schemas, the model doesn't need connector-specific training. Schema discovery provides the tables and fields, and the LLM applies general SQL reasoning to them. Reliability depends on accurate schema metadata, clear tool definitions, and prompts that prioritize tool use over guessing.

Industry use cases for ChatGPT models

Financial services and analytics

Financial institutions deploy ChatGPT for automated financial reporting, risk analysis dashboards, and regulatory compliance queries
Investment banks use the models to query trading data and generate compliance reports across multiple regulatory databases
Retail banks leverage ChatGPT for customer analytics, fraud detection pattern analysis, and loan portfolio risk assessment by connecting to transaction databases, credit scoring systems, and external economic indicators
Asset management firms deploy ChatGPT as copilots for portfolio managers, enabling quick exploration of holdings data, performance attribution, and ESG compliance checks

Healthcare and life sciences

Healthcare organizations use ChatGPT models for clinical documentation assistance, medical research synthesis, and patient data analysis while maintaining HIPAA compliance
Hospital systems enable physicians to query patient cohorts and treatment outcomes using natural language that translates into FHIR-compliant queries across EHR systems
Pharmaceutical companies use ChatGPT for clinical trial data analysis, adverse event reporting, and regulatory submission preparation
Life sciences research institutions leverage the models to synthesize scientific literature, query genomic databases, and analyze experimental results

Retail and e-commerce

Retailers leverage ChatGPT for customer behavior analysis, inventory optimization, and personalized marketing campaigns
E-commerce platforms use the models to power merchandising analytics, connecting to order management systems, inventory databases, and customer data platforms
Supply chain teams deploy ChatGPT for demand forecasting and inventory planning by querying historical sales data, promotional calendars, and supplier lead times
Marketing departments analyze campaign performance and customer lifetime value by querying advertising platforms, CRM systems, and transaction databases

Enterprise operations and business intelligence

Organizations deploy ChatGPT as intelligent copilots for business analysts, enabling self-service analytics without SQL expertise
Finance teams query ERP systems to generate budget variance reports and analyze spend patterns
Human resources departments leverage ChatGPT for workforce analytics, analyzing turnover rates, compensation benchmarks, and diversity metrics
Sales operations teams analyze pipeline health and rep performance by connecting to CRM platforms
IT operations teams use ChatGPT for infrastructure monitoring and incident analysis by querying log aggregation systems and ticketing platforms

Future perspective of ChatGPT

Reasoning and reliability improvements

OpenAI's roadmap emphasizes continued advancement in reasoning capabilities, with future iterations expected to reduce hallucinations further and improve multi-step logical consistency. The o-series models represent early progress in this direction, with subsequent releases likely expanding deep reasoning capabilities across the model family while maintaining practical latency. Future improvements include advances in chain-of-thought processing, better calibration where models express appropriate uncertainty, and improved ability to identify when additional information is needed. For Connect AI deployments, these improvements translate to higher accuracy in SQL generation, fewer retry cycles, and better handling of ambiguous user requests.

Enhanced enterprise integration

Future developments will likely focus on tighter integration with enterprise data ecosystems, including improved support for complex data governance rules, more sophisticated caching mechanisms for frequently accessed schemas, and better handling of real-time data streams. Expected enhancements include native support for enterprise authentication patterns, improved schema understanding through learning from example queries, and evolution of the Assistants API to support more sophisticated orchestration patterns like conditional workflows and automatic recovery from failures. Integration with enterprise observability platforms will improve visibility into model decision-making and cost attribution across business units.

Specialization and efficiency

The trend toward model variants like mini and nano indicates OpenAI's focus on providing specialized models optimized for specific use cases, balancing capability with cost and latency. Future releases may include vertical-specific models trained on industry terminology, task-optimized variants where some models excel at SQL generation while others focus on explanation, and deployment-specific configurations for low-latency versus high-throughput use. Efficiency improvements will address current challenges through better prompt compression, smarter caching that reuses schema information across sessions, and adaptive context windows that balance cost and capability. As the ChatGPT ecosystem matures, organizations can expect better tooling for model selection, more transparent performance benchmarks, clearer guidance on matching models to workloads, and enhanced monitoring tools that help optimize costs and benchmark performance against industry standards.

Simplify ChatGPT connectivity with CData

CData Connect AI makes it easier to connect ChatGPT with the enterprise data sources, BI tools, and analytics platforms. With direct integration, enable natural language queries against live data, eliminating manual steps and enabling automated, governed data access.

Start your free trial of CData Connect AI today! As always, our world-class Support Team is available to assist you with any questions you may have.