Using AI to manage engineering RFCs at scale

In today’s rapidly evolving engineering environments, managing Requests for Comments (RFCs) at scale has become a significant challenge for large organizations. As projects grow more complex and teams become increasingly distributed, traditional RFC workflows often struggle to maintain consistency, transparency, and speed. Artificial Intelligence (AI) offers transformative solutions to streamline the entire RFC lifecycle—from creation and review to approval and implementation—bringing efficiency, accuracy, and scalability to engineering decision-making processes.

Understanding Engineering RFCs

RFCs are formal documents that propose changes to systems, processes, or architectures. They are central to structured decision-making in engineering teams, especially in software development, systems engineering, and infrastructure design. Typically, an RFC includes a problem statement, proposed solution, rationale, and an impact assessment. While this structure brings order, managing RFCs in high-volume or high-velocity environments poses challenges:

• Manual reviews are time-consuming and inconsistent.

• Tracking the status of hundreds of RFCs is cumbersome.

• Institutional knowledge is often buried in comment threads and attachments.

• Collaboration across teams and time zones leads to delays and miscommunication.

AI addresses these challenges by automating repetitive tasks, improving documentation quality, surfacing relevant information, and enabling intelligent prioritization.

Automating RFC Drafting with Natural Language Generation

Creating RFCs from scratch can be a tedious process. Engineers often spend significant time converting their ideas into structured documents. AI-powered natural language generation (NLG) tools can assist by:

• Turning structured data (e.g., GitHub issues, Jira tickets, architecture diagrams) into draft RFCs.

• Suggesting templates and formatting based on organizational standards.

• Summarizing discussions or past incidents to pre-fill context sections.

This automation reduces cognitive load and helps teams spend more time on critical thinking rather than documentation.

Enhancing Review Workflows with AI-Powered Summarization and Analysis

RFC reviews are essential for ensuring alignment and avoiding costly mistakes. However, reviews often involve lengthy documents and time-consuming discussions. AI can assist in this phase through:

• Automated Summarization: Providing concise overviews of long RFCs, allowing reviewers to grasp key points quickly.

• Change Detection: Highlighting differences between RFC versions, helping teams focus on what has changed.

• Sentiment Analysis: Gauging reviewer sentiment to identify controversial areas needing further discussion.

• Anomaly Detection: Flagging sections that deviate significantly from best practices or previous RFCs.

These tools speed up reviews, reduce human error, and ensure a more consistent evaluation process.

Intelligent Tagging and Knowledge Management

Organizations accumulate a vast corpus of RFCs over time, creating a valuable knowledge base. Yet, without effective organization, this information remains underutilized. AI can enrich RFC metadata by:

• Automatically tagging RFCs based on content using natural language processing (NLP).

• Categorizing proposals by domain (e.g., frontend, backend, infrastructure) or type (e.g., experimental, deprecation).

• Linking related RFCs and historical decisions, enabling quick reference and pattern recognition.

This allows engineers to quickly search, retrieve, and learn from previous proposals, accelerating decision-making and avoiding redundant work.

Streamlining Approvals with Predictive Modeling

Getting an RFC approved often requires navigating complex governance structures. AI can support this process by using predictive modeling to:

• Estimate the likelihood of approval based on content, past outcomes, and reviewer profiles.

• Recommend reviewers with relevant expertise and a history of engagement with similar topics.

• Forecast review durations and flag RFCs that are stuck or delayed.

These insights help project managers proactively manage timelines and allocate resources more effectively.

Integrating with Developer Workflows

For AI-driven RFC management to be effective, it must integrate seamlessly into existing development workflows. This includes:

• ChatOps integrations: Allowing engineers to interact with the RFC system via Slack, Microsoft Teams, or Discord bots using natural language queries.

• GitOps workflows: Syncing RFCs with repositories to automatically track implementation progress and link decisions to code changes.

• Continuous documentation: Updating RFCs as systems evolve, using AI to monitor repositories and detect drift from the original design intent.

This integration minimizes context switching and makes RFCs a living part of the development lifecycle rather than static documents.

Maintaining Transparency and Trust

One concern with applying AI in decision-making processes is transparency. Engineering teams must understand how AI-driven suggestions are generated to trust and adopt them. This calls for:

• Explainable AI (XAI): Providing human-readable explanations for AI decisions, such as why a certain reviewer was recommended or why a proposal is flagged as high-risk.

• Audit Trails: Keeping logs of AI-generated changes and suggestions to enable traceability.

• Human-in-the-Loop Design: Ensuring that final decisions remain with engineers, with AI serving as an assistant rather than a gatekeeper.

By maintaining transparency, organizations can foster trust and ensure responsible use of AI in critical workflows.

Use Cases in Large-Scale Engineering Teams

Several high-performing engineering organizations have already begun integrating AI into their RFC processes:

• Meta (Facebook) uses internal AI tools to analyze and categorize design docs and automatically identify reviewers.

• GitHub Copilot is being adapted to help generate initial drafts of RFCs from commit messages and code changes.

• Stripe and Airbnb have developed internal systems that use AI to surface similar past RFCs when a new proposal is submitted.

These use cases demonstrate the tangible benefits of AI in improving engineering productivity and consistency at scale.

Challenges and Considerations

Despite its benefits, implementing AI for RFC management requires thoughtful planning. Key challenges include:

• Data Privacy and Security: RFCs often contain sensitive architectural information. AI models must be secured against leaks and misuse.

• Bias in Models: Training data may reflect historical biases in decision-making, which could skew AI suggestions.

• User Adoption: Engineers may resist AI tools if they are seen as intrusive or if they disrupt established workflows.

Addressing these challenges involves robust data governance, inclusive model training practices, and user-centric tool design.

The Future of AI-Enhanced Engineering Governance

As AI capabilities continue to evolve, the management of engineering RFCs is poised for a transformation. Future developments may include:

• Conversational RFC Assistants: AI agents that help draft, review, and update RFCs through dialogue.

• Real-time Collaboration Tools: AI-enhanced interfaces that facilitate live editing and feedback across time zones.

• Decision Impact Simulations: AI models that simulate the downstream effects of proposed changes on system performance and reliability.

These advancements will make RFCs more dynamic, data-driven, and actionable, aligning with the needs of modern engineering teams operating at scale.

By embracing AI, organizations can transform RFCs from static paperwork into intelligent, collaborative tools that drive innovation, reduce friction, and enhance technical governance across the board.