Iterating Faster on Design Docs: An AI-Assisted Approach
As detection engineers and software developers, we often face the challenge of navigating large, complex codebases when implementing new features or starting new projects. Design docs are an essential part of this process, but creating them can be both time-consuming and complex.
Why Design Documents Matter
Design documents are crucial for technical projects, serving as both planning tools and communication vehicles. They help us think through complex implementations, identify potential issues early, and align team members on approach and execution. Beyond their technical value, design documents serve a critical organizational function—they're how we achieve consensus among stakeholders, ensure alignment across teams, and create shared understanding of complex technical decisions.
However, creating comprehensive design docs can be time-consuming and iterative, often requiring deep understanding of existing systems and foresight into potential challenges. Recently, I discovered how AI assistance—specifically through tools like Claude Code—significantly accelerates the creation of design documents and migration plans.
AI-Assisted Doc Writing
Claude Code is an AI-powered command-line tool from Anthropic that allows developers to interact with their codebase through natural language. It can analyze source code, understand complex systems, and generate documentation—all while integrating directly with your development environment.
When working with unfamiliar or extensive codebases, understanding the entire system before making changes is crucial. Claude Code excels here by:
- Reading and comprehending all source code and documentation
- Generating markdown-formatted design documents that can be converted to your preferred format
- Supporting real-time editing and iteration
- Creating diagrams using Mermaid and other graph libraries
In a recent project requiring updates to a large Python codebase I was only partially familiar with, I asked Claude Code to produce a design document addressing an initial set of questions that I provided. It analyzed the codebase and documentation, producing a barebones technical document that enabled me to iteratively refine in it in collaboration with the agent.
Streamlining Complex Migrations
Technical migrations often involve more than just deploying code—they require carefully orchestrated steps to ensure safe rollouts. Recently, I needed to migrate a bunch of regional Databricks clusters from Hive Metastore to Databricks Unity Catalog. Working with Claude in a Terraform codebase, I was able to produce a plan to run the migration, update the source code iteratively as needed to ensure success, and work through each migration step one by one for a smooth transition.
What impressed me most was Claude Code's ability to:
- Read and edit the codebase while I edited deployment plans we were co-authoring
- Propose necessary code changes when things didn't go as planned
- Build comprehensive migration plans including cleanup steps
Benefits and Limitations
Using AI assistance for design documentation offers several key advantages. It helps identify potential errors early in the process, before implementation has begun. The AI often surfaces considerations that might have been overlooked in your initial planning, bringing a fresh perspective to the design. Perhaps most importantly, it dramatically accelerates the overall design process, allowing teams to move from conception to implementation faster without sacrificing documentation quality.
However, these tools do have their limitations. Claude Code is only as effective as the information you provide it—incomplete context inevitably leads to incorrect suggestions. The tool works best when you've already established some direction and can provide specific questions or requirements. And while the AI can generate impressive documentation, human review remains essential; the output should be treated as a strong first draft rather than a finished product.
The Importance of Continued Design Documentation
Despite the efficiency gains from AI assistance, it's crucial that we continue writing design documents rather than abandoning the practice altogether. Design docs remain one of the most effective ways to achieve organizational consensus on technical decisions. They force us to articulate our thinking clearly, consider edge cases, and create artifacts that can be reviewed, discussed, and refined by the broader team.
The collaborative nature of design documentation—where multiple stakeholders can review, comment, and suggest improvements—cannot be replicated by AI alone. These documents serve as the foundation for technical discussions and help ensure that everyone is aligned before significant development effort begins.
Shifting Review Patterns and Deeper Validation
One interesting change I've noticed in my workflow is that I now spend more time reviewing AI-generated design documents than I used to spend writing them from scratch. This shift brings both opportunities and challenges.
On one hand, the review process allows me to focus on higher-level architectural decisions rather than getting bogged down in the mechanics of document creation. On the other hand, this approach requires going one level deeper in validation—we can't simply trust that the AI has correctly interpreted requirements or identified all relevant considerations.
A particular concern is the potential for a "thought funnel" effect, where I might not consider approaches or solutions that I would have discovered through the deeper thinking process of writing the document myself. When relying on AI output, there's a risk of missing innovative approaches or overlooking important considerations that would have emerged through more intensive personal engagement with the problem space.
This means that everything in an AI-generated design document needs more thorough validation. Rather than trusting human expertise and experience, we need to verify that the AI has correctly understood the context, identified the right problems, and proposed appropriate solutions. This additional validation step is time-consuming but essential for maintaining quality and ensuring that we don't miss critical insights.
Conclusion
For cybersecurity and software teams working with complex systems, Claude Code offers a powerful way to accelerate design documentation and migration planning. By leveraging AI to understand and document codebases, we can focus more on implementation and less on preparation—ultimately delivering better, more reliable systems faster.
However, the key is finding the right balance. AI assistance should enhance our design process, not replace critical thinking. We must continue to prioritize design documentation as a means of achieving organizational consensus while being mindful of the additional review and validation work required when working with AI-generated content. The goal isn't to eliminate human expertise from the design process, but to augment it in ways that make us more effective while maintaining the rigor and thoughtfulness that good design documentation requires.