Introduction

Fourth-generation programming languages (4GLs) were created to simplify software development, reduce code complexity, and improve productivity. Unlike traditional third-generation languages (3GLs) such as C, Java, or Python, 4GLs focus on what the system should do, rather than how it should do it.

With the rapid rise of artificial intelligence, an important question emerges:

Do 4GL programming languages include artificial intelligence capabilities as well?

This article explores what 4GL languages are, how they work, and whether AI is built into or supported by modern 4GL platforms.

What Is a 4GL Programming Language?

A 4GL (Fourth-Generation Language) is a high-level programming language designed to be:

  • More declarative than procedural

  • Easier to read and write

  • Closer to human language

  • Focused on productivity rather than low-level logic

4GLs are commonly used in:

  • Database querying

  • Report generation

  • Business applications

  • Rapid application development (RAD)

Examples of 4GL Languages and Platforms

  • SQL (often considered a partial 4GL)

  • MATLAB

  • SAS

  • ABAP (SAP)

  • Informix-4GL

  • Oracle Forms

  • Low-code / no-code platforms (modern evolution)

How 4GL Differs From 3GL

Feature 3GL 4GL
Programming style Procedural Declarative
Code volume High Low
Abstraction level Medium High
Developer productivity Moderate High
Control over system High Limited

4GLs trade fine-grained control for speed, simplicity, and abstraction.

Does 4GL Include Artificial Intelligence?

Short Answer

Traditional 4GL languages do not natively include artificial intelligence.

However, modern 4GL platforms increasingly integrate AI features.

Why Traditional 4GL Did Not Include AI

Early 4GL languages were designed primarily for:

  • Data processing

  • Business logic

  • Reporting and forms

  • Database interaction

At the time of their creation:

  • AI research was limited

  • Compute power was expensive

  • Machine learning frameworks did not exist

As a result, AI was outside the original scope of 4GL design.

How Modern 4GL Platforms Include AI Today

While classic 4GLs lack built-in AI, modern platforms extend 4GL concepts by embedding AI services and automation.

1. Built-In AI Services

Many low-code / 4GL platforms now provide:

  • Predictive analytics

  • Natural language processing

  • Recommendation engines

  • Image and text recognition

These capabilities are often exposed as visual components or high-level functions, consistent with 4GL principles.

2. Integration With AI and ML Models

Modern 4GL platforms commonly:

  • Call external AI APIs

  • Integrate with Python or R models

  • Use cloud-based AI services

  • Connect to machine learning pipelines

This allows developers to consume AI without implementing algorithms manually.

3. AI-Assisted Development

Some platforms now use AI to:

  • Generate code automatically

  • Suggest workflows

  • Optimize queries

  • Detect errors and anomalies

This represents AI embedded into the development process itself, not just the application output.

Is AI a Natural Fit for 4GL?

Yes — conceptually, AI aligns well with 4GL philosophy.

Shared Characteristics

  • High-level abstraction

  • Focus on outcomes rather than implementation

  • Reduced manual coding

  • Increased productivity

AI essentially acts as a fifth-generation capability layered on top of 4GL systems.

4GL, AI, and the Evolution Toward 5GL

Some experts argue that:

  • Traditional 4GL + AI integration

  • Declarative intent-based programming

  • Natural language interfaces

are steps toward fifth-generation programming languages (5GLs).

In this context:

  • 4GL provides structure and abstraction

  • AI provides reasoning, learning, and automation

Practical Use Cases of 4GL With AI

  • Business intelligence dashboards with predictive insights

  • Automated reporting with anomaly detection

  • Enterprise applications with recommendation systems

  • Workflow automation with intelligent decision rules

  • Natural language query interfaces over databases

Limitations of AI in 4GL Environments

Despite advantages, limitations remain:

  • Less control over model internals

  • Dependency on external AI services

  • Performance constraints

  • Vendor lock-in risks

Organizations must evaluate trade-offs carefully.

Final Verdict

Traditional 4GL programming languages do not inherently include artificial intelligence.
However, modern 4GL and low-code platforms increasingly integrate AI capabilities through built-in services, APIs, and automation.

Rather than replacing 4GL, AI extends it, making software development faster, more accessible, and more intelligent.

In practice, 4GL plus AI represents a powerful bridge between traditional programming and the future of intelligent systems.

FAQs

Is SQL considered a 4GL?

SQL is often classified as a declarative or partial 4GL due to its high-level abstraction.

Can AI be written directly in 4GL?

Typically no. AI models are developed externally and integrated into 4GL platforms.

Are low-code platforms a type of 4GL?

Yes. Modern low-code platforms are considered an evolution of 4GL concepts.