Large Language Models (LLMs) in Systems Engineering

Introduction

The rapid rise of Artificial Intelligence (AI) and Large Language Models (LLMs) is transforming the way industries approach systems engineering. Traditionally, systems engineering has relied on manual processes for requirements definition, elicitation, specification, and traceability, tasks that are often time-consuming and prone to ambiguity. Today, Generative AI in systems engineering is bridging this gap by automating repetitive tasks, improving accuracy, and enabling end-to-end requirements lifecycle management.

LLMs such as OpenAI GPT, IBM Watson, Google DeepMind, Microsoft Azure AI, and Valispace AI are increasingly being adopted by engineering teams across aerospace, defense, automotive, medical devices, and IT systems engineering. These AI-powered solutions help organizations accelerate requirements management, ensure compliance and certification, and improve traceability in Agile and MBSE (Model-Based Systems Engineering) environments.

For global engineering organizations in regions like the USA, Europe, Germany, UK, and India, the integration of LLMs in systems engineering offers a competitive advantage: reducing project delays, minimizing errors, and enhancing collaboration across distributed teams.

In this article, we’ll explore the applications, benefits, challenges, best practices, and future of LLMs in systems engineering, while comparing leading AI requirements engineering software platforms such as Visure Requirements ALM with AI assistance, IBM DOORS, and others.

What are Large Language Models (LLMs) in Systems Engineering?

Large Language Models (LLMs) in systems engineering are advanced AI algorithms trained on vast amounts of technical and natural language data to support tasks such as requirements definition, elicitation, specification, verification, and traceability. In practice, LLMs act as intelligent assistants that can read, interpret, and generate engineering documents, helping teams reduce ambiguity, automate repetitive tasks, and streamline collaboration.

For example, an LLM can transform unstructured stakeholder input into clear requirements specifications, suggest improvements for requirements quality, or enable real-time traceability across the entire requirements lifecycle. This makes them highly valuable in safety-critical industries such as aerospace, defense, automotive, and medical devices.

AI in Systems Engineering and Generative AI in Engineering

AI in systems engineering extends beyond automation, it enhances model-based systems engineering (MBSE), Agile requirements engineering, and compliance management by learning patterns and optimizing decision-making.

Meanwhile, generative AI in engineering brings the ability to simulate design alternatives, generate requirement drafts, and even predict risks. By leveraging LLMs within engineering projects, organizations can achieve faster development cycles, reduce human error, and maintain end-to-end requirements lifecycle coverage.

Importance of AI-Powered Requirements Engineering Tools

The adoption of AI-powered requirements engineering tools is becoming essential for organizations handling complex projects with strict regulatory requirements. These tools, such as Visure Requirements ALM with AI assistance, IBM DOORS, Valispace AI, and others, help teams:

• Automate requirements capture and specification.
• Improve requirements traceability and version control.
• Support requirements reusability strategies.
• Ensure compliance and certification readiness across domains.
• Provide Agile requirements gathering solutions for distributed teams.

By integrating LLMs into requirements engineering software, companies in regions like the USA, Europe, Germany, the UK, and India gain a strategic edge, enhancing productivity, lowering risks, and achieving real-time live traceability across the requirements engineering lifecycle.

Large Language Models (LLMs) in systems engineering are AI-driven tools that automate requirements definition, specification, and traceability, enabling faster, more accurate, and compliant systems design across industries like aerospace, defense, automotive, and medical devices.

Role of LLMs in the Requirements Engineering Lifecycle

The requirements engineering lifecycle covers key stages, requirements definition, elicitation, specification, verification, and validation, which ensure that systems meet stakeholder needs while maintaining compliance. Traditionally, these processes are manual, error-prone, and time-intensive. By integrating Large Language Models (LLMs) in systems engineering, organizations can achieve end-to-end requirements lifecycle coverage with higher efficiency, accuracy, and agility.

Requirements Definition with AI

Defining requirements is often the most critical step in systems engineering. LLMs simplify requirements definition by:

• Analyzing unstructured documents, stakeholder inputs, and regulations.
• Suggesting structured, unambiguous requirement statements.
• Detecting gaps, redundancies, or ambiguities early.

This reduces common mistakes in defining requirements and ensures teams establish a strong foundation for the project.

Requirements Elicitation Using LLMs – Capturing Stakeholder Needs

Requirements elicitation involves gathering stakeholder expectations, which are often expressed in natural language. LLMs enable AI-assisted requirements elicitation by:

• Translating informal discussions, interviews, or emails into well-structured requirements.
• Identifying conflicting needs across distributed teams.
• Supporting Agile requirements gathering tools for real-time collaboration.

This helps organizations, especially globally align stakeholders faster and more effectively.

Requirements Specification Automation – Ensuring Clarity and Precision

Requirements specification demands precision to avoid misinterpretation. AI-powered requirements engineering tools with LLMs:

• Automatically draft requirement specifications in compliance with industry standards.
• Standardize terminology across global teams.
• Enable requirements reusability strategies by recommending reusable components.

This results in improved software requirements management and supports certification in safety-critical domains such as aerospace, defense, and medical devices.

AI for Requirements Verification and Validation

Verification and validation (V&V) ensure that requirements are not only correctly defined but also fulfill stakeholder needs. LLMs support V&V by:

• Cross-checking requirements against regulatory frameworks.
• Automating consistency checks across the requirements traceability matrix.
• Generating test cases aligned with system requirements.

By providing live traceability, LLMs help teams maintain compliance, reduce errors, and accelerate audits.

LLMs play a vital role in the requirements engineering lifecycle by simplifying requirements definition, enabling AI-driven requirements elicitation, automating specification for clarity and precision, and supporting verification and validation with live traceability, ensuring full requirements lifecycle coverage.

Applications of LLMs in Model-Based Systems Engineering (MBSE)

Model-Based Systems Engineering (MBSE) relies on structured models rather than documents to define, design, and manage complex systems. Integrating Large Language Models (LLMs) in MBSE enhances these processes by bridging natural language inputs with formal system models. This allows engineering teams to streamline communication, reduce errors, and accelerate design validation.

Use of LLMs in MBSE

LLMs in MBSE act as intelligent assistants that:

• Translate natural language requirements into formal system models.
• Support engineers by generating model annotations and design constraints.
• Simplify the mapping of stakeholder requirements to system architecture.

This ensures a seamless connection between requirements engineering and system modeling, which is critical in industries with strict compliance and certification needs.

AI-Driven Systems Design and Simulation

Generative AI in systems engineering supports system architects by:

• Suggesting alternative design configurations.
• Running AI-driven simulations to predict performance and detect risks.
• Supporting early design validation before costly physical testing.

By using LLMs for AI-driven systems design, organizations achieve faster iteration cycles, reduce rework, and optimize system performance.

Enhancing Requirements Traceability in Systems Engineering

Traceability is a cornerstone of requirements lifecycle management. LLMs strengthen this process by:

• Automatically linking requirements to MBSE artifacts, test cases, and compliance standards.
• Supporting live traceability across the systems engineering lifecycle.
• Identifying missing links or inconsistencies in the traceability matrix.

This ensures end-to-end requirements coverage and minimizes risks in complex, safety-critical projects.

Real-World Applications of LLMs in Aerospace, Defense, and Automotive Engineering

LLMs are already being adopted in industries where MBSE is mandatory:

• Aerospace & Defense: Automating certification documentation, enabling real-time traceability, and ensuring compliance with DO-178C, DO-254, and MIL-STD standards.
• Automotive: Supporting functional safety requirements (ISO 26262), improving design validation, and enabling Agile development cycles for autonomous vehicles.
• Medical Devices & Industrial Systems: Assisting in FDA/IEC compliance, generating risk analysis reports, and enhancing traceability across product lifecycle stages.

Engineering organizations globally are adopting LLM-powered MBSE tools like Visure Requirements ALM with AI assistance, IBM DOORS, Valispace AI, and Microsoft Azure AI to accelerate system development while maintaining compliance.

LLMs in Model-Based Systems Engineering (MBSE) are used to translate natural language into system models, automate AI-driven systems design and simulation, enhance requirements traceability, and support compliance in industries like aerospace, defense, and automotive.

Benefits of Large Language Models for Engineering Teams

The integration of Large Language Models (LLMs) in systems engineering provides engineering teams with measurable advantages across the requirements engineering lifecycle. From requirements management to compliance and certification, LLMs enhance efficiency, accuracy, and agility, making them indispensable in modern engineering workflows.

Improved Efficiency in Requirements Management with AI

LLMs significantly enhance requirements management by:

• Automating requirements definition, elicitation, and specification.
• Detecting inconsistencies and ambiguities early.
• Reducing manual rework and time spent on documentation.

By using AI requirements engineering software, teams improve collaboration, cut project costs, and accelerate delivery.

End-to-End Requirements Lifecycle Management with AI

With LLMs, organizations can achieve full requirements lifecycle coverage, including:

• Requirements capture → specification → traceability → verification and validation.
• Seamless integration with MBSE tools and Agile frameworks.
• Enhanced requirements version control for distributed global teams.

This ensures end-to-end requirements management, minimizing risks and improving system reliability.

Agile Requirements Engineering with AI and Agile Requirements Gathering Tools

In Agile environments, LLMs act as intelligent assistants by:

• Supporting Agile requirements gathering solutions to capture user stories in real time.
• Converting informal stakeholder conversations into structured backlog items.
• Enabling Agile requirements development workflows with traceability across sprints.

Faster Compliance and Certification Processes Using LLMs

Compliance is one of the biggest challenges in industries like aerospace, defense, automotive, and medical devices. LLMs accelerate certification readiness by:

• Generating audit-ready documentation.
• Mapping requirements automatically to industry standards (DO-178C, ISO 26262, IEC 62304, etc.).
• Maintaining live traceability for real-time compliance reporting.

This reduces delays, improves accuracy, and ensures organizations meet stringent regulatory requirements efficiently.

LLMs benefit engineering teams by improving efficiency in requirements management, enabling end-to-end lifecycle coverage, supporting Agile requirements engineering, and accelerating compliance and certification processes across industries like aerospace, defense, automotive, and medical devices.

Challenges and Limitations of LLMs in Systems Engineering

While Large Language Models (LLMs) in systems engineering offer significant benefits, organizations must address key challenges before fully relying on AI-powered solutions. Issues such as data quality, bias, accuracy, and tool integration can affect the success of LLM adoption across the requirements engineering lifecycle.

Data Quality and Ambiguity in Requirements Specification

• LLMs rely on high-quality input; poorly written or ambiguous requirements can lead to incorrect outputs.
• Inconsistent terminology across teams may cause misinterpretations in the requirements specification.
• Without proper governance, AI-generated requirements risk introducing ambiguity and compliance gaps.

This makes requirements review processes and human oversight essential.

Challenges of Using LLMs in Engineering Projects (Bias, Accuracy, Validation)

• LLMs may introduce bias if trained on incomplete or unbalanced datasets.
• Accuracy remains a concern; AI-driven requirements engineering still needs expert validation.
• Requirements verification and validation cannot be fully automated—human engineers must cross-check AI suggestions.

This is particularly critical in safety-critical industries like aerospace, defense, and automotive, where errors can lead to certification failures.

Integration Challenges with Legacy Requirements Management Tools

• Many organizations still rely on legacy requirements management systems such as IBM DOORS.
• Integrating LLMs with these older platforms can be complex, requiring custom APIs or middleware.
• Transitioning to modern requirements engineering platforms like Visure Requirements ALM with AI assistance helps overcome these barriers but requires careful planning and migration strategies.

Organizations in regions like the USA, Europe, and Germany often face these integration hurdles when modernizing their systems engineering toolchains.

The main challenges of using LLMs in systems engineering include data quality and ambiguity in requirements specification, risks of bias and accuracy issues in AI-generated outputs, and integration difficulties with legacy requirements management tools such as IBM DOORS.

Best Practices for Applying LLMs in Systems Engineering

To maximize the value of Large Language Models (LLMs) in systems engineering, organizations must adopt structured strategies. These best practices focus on requirements reusability, traceability, and Agile workflows, ensuring end-to-end requirements lifecycle coverage with accuracy and compliance.

Strategies for Requirements Reusability with AI

• Use AI-powered requirements engineering tools to identify and store reusable requirement components.
• Apply requirements reusability strategies across projects to reduce redundancy and accelerate delivery.
• Leverage LLMs to suggest reusable requirements templates aligned with compliance standards.

This improves consistency and lowers costs in industries such as aerospace, defense, automotive, and medical devices.

Traceability Best Practices with LLMs

• Ensure requirements traceability from definition through verification and validation.
• Use LLMs to automatically generate and maintain a traceability matrix.
• Regularly review AI-generated links to avoid errors in compliance-critical projects.

Following traceability best practices ensures end-to-end requirements management and supports certification readiness.

Leveraging Real-Time Traceability vs. Late Traceability

• Real-time (live) traceability with LLMs enables instant tracking of changes across the requirements engineering lifecycle.
• Unlike late traceability, which causes delays and gaps, live traceability reduces risks and improves decision-making.
• Tools like Visure Requirements ALM with AI assistance provide live traceability benefits for global engineering teams in the USA, Europe, Germany, UK, and India.

Building Agile Requirements Development Workflows with LLMs

• Integrate Agile requirements engineering with AI to capture evolving stakeholder needs.
• Use Agile requirements gathering tools powered by LLMs to transform informal inputs into structured backlog items.
• Align AI-driven insights with sprint planning and continuous delivery pipelines.

This strengthens collaboration, supports Agile requirements development, and accelerates system delivery.

Best practices for applying LLMs in systems engineering include adopting AI strategies for requirements reusability, following traceability best practices, leveraging real-time traceability over late traceability, and building Agile requirements development workflows with LLM-powered tools.

Comparing LLM-Based Requirements Engineering Tools

The growing adoption of AI-powered requirements engineering software has led to a variety of Large Language Model (LLM)-based platforms for systems engineering. While many solutions exist, the effectiveness of each depends on its ability to deliver end-to-end requirements lifecycle management, traceability, and compliance support for industries such as aerospace, defense, automotive, and medical devices.

Visure Solutions LLM for Systems Engineering

Visure Requirements ALM with AI assistance stands out as one of the most comprehensive requirements engineering platforms. It provides:

• AI-assisted requirements definition, elicitation, and specification.
• Live traceability across the entire requirements lifecycle.
• Automated requirements review to detect ambiguity and improve quality.
• Compliance templates for standards such as DO-178C, ISO 26262, IEC 62304, and more.
• Seamless integration with MBSE and Agile workflows.

Engineering organizations in the USA, Europe, Germany, UK, and India rely on Visure for its full requirements lifecycle coverage and AI-driven automation, making it a strong alternative to older legacy tools.

Selecting the Best AI-Driven Systems Engineering Platforms

When selecting the best AI-driven systems engineering tool, organizations should evaluate:

• Requirements lifecycle coverage (definition, specification, traceability, verification, validation).
• Agile requirements engineering support.
• Compliance readiness for industry standards.
• Scalability and integration with MBSE, DevOps, and legacy systems.

Based on these criteria, Visure Requirements ALM with AI assistance emerges as the best requirements management platform for enterprises that need both AI automation and certification support, especially in safety-critical sectors.

Future of AI and Large Language Models in Systems Engineering

The future of systems engineering is being shaped by Artificial Intelligence (AI) and Large Language Models (LLMs), with a strong focus on automation, traceability, and compliance. As engineering projects grow more complex across aerospace, defense, automotive, and medical devices, the integration of generative AI in systems engineering will redefine how organizations manage the requirements engineering lifecycle.

AI-Assisted MBSE and Real-Time Traceability

Model-Based Systems Engineering (MBSE) is evolving with LLMs that support real-time requirements traceability. Instead of static documentation, engineering teams can leverage AI-powered systems engineering platforms to dynamically link requirements, models, and test cases. This ensures live traceability vs. late traceability, reducing errors and improving end-to-end requirements lifecycle coverage.

Generative AI in Systems Design and Simulation

The use of generative AI in engineering will enable teams to create system designs, architecture models, and simulations automatically. By analyzing historical project data and specifications, LLMs can generate optimized design alternatives, validate requirements against safety standards, and accelerate Agile requirements development.

Continuous Compliance in Agile and DevOps Environments

With industries moving toward Agile and DevOps, AI requirements engineering tools will play a key role in continuous compliance. LLMs can automatically map requirements to industry standards such as DO-178C, ISO 26262, IEC 62304, and ARP4754A, ensuring that compliance is maintained throughout iterative development cycles. This reduces rework and accelerates certification processes in highly regulated industries.

Role of LLMs in the Future of Requirements Management Software

Future requirements management tools will integrate LLM-powered assistants to provide:

• Automated requirements specification with clarity and precision.
• AI-driven requirements validation and verification.
• Agile requirements gathering tools for cross-functional teams.
• Requirements reusability strategies to minimize duplication.

Platforms like Visure Requirements ALM with AI assistance are already paving the way by combining AI automation, MBSE integration, and compliance support, making them the benchmark for the next generation of requirements engineering software.

The future of AI and Large Language Models (LLMs) in systems engineering lies in AI-assisted MBSE, real-time requirements traceability, generative AI-driven design and simulation, and continuous compliance in Agile and DevOps environments. Advanced requirements management platforms like Visure are leading this shift by integrating LLMs into the full requirements lifecycle.

Conclusion

The integration of Large Language Models (LLMs) in systems engineering marks a major shift toward smarter, faster, and more reliable requirements management processes. By enhancing requirements definition, elicitation, specification, traceability, verification, and validation, LLMs enable engineering teams to achieve end-to-end requirements lifecycle coverage with unprecedented efficiency.

From AI-assisted MBSE and generative systems design to real-time traceability and continuous compliance in Agile and DevOps environments, the role of AI in systems engineering will only continue to expand. While challenges such as data quality, ambiguity, and tool integration remain, the benefits of AI-driven requirements engineering software far outweigh the limitations.

For industries like aerospace, defense, automotive, medical devices, and IT systems engineering, adopting AI-powered requirements engineering tools is no longer optional, it’s essential for staying competitive in a rapidly evolving landscape.

Check out the 30-day free trial at Visure and experience how Visure Requirements ALM with AI assistance can transform your requirements lifecycle management with automation, compliance, and live traceability.