Bridging Context Engineering in AI with Requirements Engineering

How Emerging AI Research Could Reinvent Context Scenarios in Software Design

Hey there, fellow software enthusiasts! If you’re like me, you’ve probably spent countless hours crafting context scenarios to nail down requirements in software development projects. These narrative-driven descriptions of user interactions in specific situations provide a rock-solid foundation for understanding what a system really needs to do. They’re invaluable for avoiding scope creep, aligning stakeholders, and ensuring the end product actually solves real-world problems.

Well, imagine my excitement when I stumbled upon the paper “Context Engineering 2.0: The Context of Context Engineering” by Qishuo Hua and colleagues (arXiv:2510.26493v1, published just a few days ago on October 30, 2025). This gem feels like it was written just for us—exploring how machines can better grasp human intent through “context engineering.” It ties directly into our world of requirements engineering (RE), offering fresh ideas on how AI can supercharge context scenarios. Let’s break down this paper, connect the insights to RE, and explore how these concepts could be integrated into our daily workflows.

A Brief Exploration of the Paper: What Exactly is Context Engineering?

The authors kick off with a philosophical nod to Karl Marx: humans are shaped by their social relations—or, in modern terms, their contexts. Extending this to AI, they argue that machines need to understand our situations and purposes to interact effectively. Context engineering, they say, is the systematic process of designing, collecting, managing, and using contextual information to make that happen.

Key highlights from the paper:

• Historical Evolution: Context engineering isn’t new—it’s been around for over 20 years! The authors outline four eras tied to machine intelligence levels:
• Era 1.0 (1990s–2020): Primitive computers relied on structured inputs like sensors in ubiquitous computing. Think rule-based systems where humans had to “translate” intent into machine-readable formats.
• Era 2.0 (2020–Present): With LLMs like GPT-3, agents handle natural language and ambiguity. Techniques like prompt engineering, RAG (retrieval-augmented generation), and tool calling dominate.
• Era 3.0 (Future): Human-level AI that senses subtle cues like emotions for seamless collaboration.
• Era 4.0 (Speculative): Superhuman AI that proactively constructs contexts and uncovers hidden human needs.
• Formal Definition: Context is “any information that characterizes the situation of relevant entities in a user-application interaction.” Engineering it involves operations like collection (e.g., sensors), management (e.g., compression into summaries or vectors), and usage (e.g., sharing across agents).
• Design Considerations: The paper breaks this down into three pillars:
• Collection & Storage: From single-device logs in Era 1.0 to multimodal, distributed systems today. Future: Human-level ecosystems with tactile/smell sensors.
• Management: Processing text/multimodal data, organizing into layered memory (short-term vs. long-term), and abstracting (“self-baking”) raw context into compact forms.
• Usage: Sharing within/across systems, selecting relevant context, inferring user needs proactively, and preserving lifelong context.
• Applications & Challenges: Examples include AI CLIs (like Gemini CLI for code agents), deep research tools, and brain-computer interfaces. Challenges? Scalability, attention collapse in long contexts, and evaluation gaps.

The paper’s core insight: Context engineering reduces “entropy” (uncertainty) in human-machine communication. Smarter machines mean less human effort—echoing how we in RE use scenarios to clarify ambiguous requirements.

Why This Paper Hits Home for Requirements Engineering

As someone who’s relied on context scenarios to elicit requirements, this paper feels like a natural extension. In RE, context scenarios (sometimes called use case narratives or personas-in-action) describe who is using the system, what they’re trying to achieve, where and when it happens, and why it matters. They’re all about capturing the situational context to derive functional and non-functional requirements.

Here’s how the paper’s ideas map to—and could enhance—our RE practices:

• From Static Scenarios to Dynamic AI-Generated Contexts: Traditional context scenarios are human-crafted, often based on interviews or workshops. But imagine using AI agents (Era 2.0 style) to generate scenarios proactively. By feeding an LLM historical project data, user behaviors, and environmental factors, it could infer hidden needs—like the paper’s “proactive user need inference.” For example, if stakeholders describe a vague “user login” process, the AI could pull in multimodal context (e.g., device sensors for mobile vs. desktop) to suggest scenarios accounting for edge cases like poor connectivity.
• Layered Memory for Requirements Traceability: The paper’s hierarchical memory (short-term for recent interactions, long-term for abstracted knowledge) mirrors RE traceability matrices. We could build “context-aware RE tools” that store raw stakeholder inputs in short-term memory, then “self-bake” them into abstracted requirements. This would help manage large-scale projects, reducing entropy by filtering irrelevant details—much like the paper’s semantic relevance and logical dependency filters.
• Multimodal Context for Richer Scenarios: RE often sticks to text-based scenarios, but the paper pushes for multimodal inputs (text, images, audio). In software dev, this could mean integrating user session videos or sensor data from prototypes to enrich scenarios. Tools like brain-computer interfaces (futuristic, but intriguing) could even capture unspoken user frustrations, leading to more empathetic requirements.
• Overcoming RE Challenges with AI Evolution: The paper warns of “context overload” in long interactions—sound familiar? In RE, workshops can drown in details. Future Eras 3.0/4.0 suggest AI collaborators that not only understand but construct contexts, perhaps auto-refining scenarios based on inferred team dynamics. This could slash the “effort” we put into RE, making it more efficient.

In short, context engineering in AI isn’t just for machines—it’s a toolkit to make our RE processes smarter, more adaptive, and less prone to miscommunication.

Practical Takeaways: How to Apply This in Your Next Project

Ready to experiment? Here’s a starter guide inspired by the paper:

1. Adopt Prompt Engineering for Scenario Generation: Use LLMs like Grok or ChatGPT with structured prompts to brainstorm context scenarios. Example: “Generate a context scenario for a banking app user withdrawing cash during a network outage, including environmental factors and user emotions.”
2. Build a “Context Hierarchy” in Your RE Tools: Use tools like Jira or ReqView to layer requirements—raw notes at the bottom, abstracted scenarios in the middle, and high-level goals at the top. Tag them for semantic search.
3. Proactively Infer Needs: In stakeholder meetings, note patterns (e.g., repeated questions about security) and use AI to suggest unstated requirements, echoing the paper’s inference techniques.
4. Experiment with Multimodal RE: Incorporate images or voice notes into scenarios. Tools like Figma for prototypes can help visualize contexts.

The paper reminds us: As AI gets smarter, our role shifts from “translators” of intent to collaborators. In RE, that means fewer misunderstandings and more innovative software.

Wrapping Up: A Stepping Stone to Smarter Development

This paper goes beyond theory—it’s a wake-up call to reconsider how we manage context in AI and beyond. For those of us in requirements engineering, it’s a crucial reminder that context scenarios are dynamic and can evolve with AI to build stronger systems. If you’re exploring AI-assisted development, check out this arXiv paper—it’s full of insights that could revolutionize your next sprint.

What do you think? Have you used AI for generating context scenarios yet? Drop a comment below—I’d love to hear your experiences!

Please note that all quotes and summaries are taken from the paper for educational purposes. For a deeper understanding, explore the full text available on arXiv.