The Software World and Artificial Intelligence

Revolution or Evolution? A New Paradigm Beyond Boundaries

Artificial Intelligence (AI) is rising as a powerful technological wave transforming software development processes. Particularly, Large Language Models (LLMs) are creating new opportunities across a spectrum from simple applications to complex workflows. Tools based on the “agent” concept, such as Langchain, Semantic Kernel, and N8N, strive to materialize this potential. However, the inherent limitations of LLMs and challenges encountered in large-scale projects necessitate a cautious approach. This article examines the current state of AI-assisted software development, the pros and cons of LLMs, and introduces Small Language Models (SLMs) as an emerging solution.

1. The Rise of LLMs: Expectations vs. Realities

Solutions provided by startups like Cursor AI IDE and Anthropic symbolize AI’s transformative impact on software development. However, closer analysis reveals:

• Effectiveness in Small Applications: While successful in smaller projects, LLMs struggle to maintain context in complex scenarios.
• Context Loss and Repetitive Errors: As projects grow, LLMs frequently lose context, repeat similar mistakes, and require constant reprompting.

These limitations indicate LLMs alone aren’t sufficient for comprehensive software development needs.

2. Langchain, Semantic Kernel, and MCP

Tools such as Langchain and Semantic Kernel have been designed to deliver more consistent and context-aware results from LLMs. Nonetheless:

• Inconsistency: Receiving varied responses to identical prompts threatens application stability.
• Complex Prompt Management: Mechanisms like function calls and auto-invoke can complicate processes.

Model-Context-Protocol (MCP) attempts to address these issues, but core limitations persist, primarily due to the lack of memory and learning capabilities in LLMs, creating sustainability issues.

3. Memory Issue in LLMs: “Forgetfulness”

The lack of memory in LLMs restricts their ability to achieve persistent learning:

• Continuous Re-training: Models require repetitive prompting.
• Instability and Versioning Issues: New model versions can become incompatible with older prompts.

4. Inefficient Use of Large Models: Using a Truck to Carry Passengers

Large Language Models, encompassing vast knowledge bases, suffer from unnecessary data overload:

• High Cost and Low Efficiency: Excessively costly and inefficient for simple tasks.
• Privacy Risks: Large, unnecessary datasets can pose security threats.

5. Small Language Models (SLMs): Focused and Efficient Solutions

SLMs are lightweight, specifically-trained models focused on particular purposes:

• Single SLM Approach: The model is trained exclusively with functional data like function calls and orchestration.
• Function Definition and Orchestration: Instead of separate models per application, a single SLM can easily incorporate or remove functionalities.

This method reduces costs, enhances performance, and streamlines software processes.

6. New Software Architecture: Function Calls and Orchestration

In this novel architecture:

• Function Definition and SLM Integration: Simple functions introduced to SLM can be swiftly managed.
• Protocol Independence: Supports various communication protocols (HTTP, TCP, WebSocket).
• Alignment with Agile: Easily adding or removing functions makes software processes quicker and more sustainable.

7. Transforming the Developer’s Role: Strategic and Architectural Focus

Developers now focus more on strategic planning and architectural design rather than coding:

• Reduced Complexity: Eliminates the need for complex architectures, focusing on simple functions.
• Increased Creativity and Strategy: As AI handles routine tasks, developers can focus on innovation and optimization.

8. Persistent Memory and Learning Challenges

Both LLMs and SLMs still lack genuine learning capabilities:

• Error Repetition: Models fail to learn from past mistakes, needing continuous human intervention.
• Limited Error Variability: Narrow-scope SLMs produce fewer errors, but their learning mechanisms remain constrained by manual interventions.

Long-term, persistent learning capabilities promise significant improvements.

9. Conclusion: Right Technology, Right Application

AI-supported software development represents a rational evolutionary rather than revolutionary process. Instead of approaching every challenge with large models, purpose-designed smaller models (SLMs) can provide sustainable and efficient solutions. This approach reduces costs, enhances performance, and simplifies software development processes. Developers’ roles evolve strategically and architecturally, setting the stage for a new paradigm.

This rational AI-driven approach provides a robust and sustainable foundation for the future of software development.