Meaghan Choi (cloudflare): CLIs and LLMs: The Renaissance of the Prompt Based Experiences

This talk was presented at the AI The Docs online conference on April 4, 2024. We are thrilled to share the recording and the summary with you.

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Meaghan Choi

Product Design Lead at Cloudflare

Meaghan's presentation

Meaghan discusses the intersections between command line interfaces (CLIs), large language models (LLMs), and developer experience (DX). Drawing from her extensive background in designing for emerging technologies, she emphasizes the importance of leveraging lessons from past technologies to enhance future innovations, particularly in AI-driven environments.

Key Takeaways

Understanding Developer Experience (DX)

• Complex Touchpoints: Developers use various interfaces like dashboards, command lines, docs, and forums, each designed to understand developer intent and facilitate appropriate actions or information.
• Twofold Framework: Meaghan's framework involves a cohesive set of capabilities for building products and intuitive interfaces that align with developers' needs.
   

AI's Role in Developer Experience

• Revolutionizing Workflows: AI is transforming development workflows by automating tasks and integrating new services, enhancing productivity and opening up previously unfeasible possibilities.
• Chatbots and CLIs: Meaghan draws a parallel between modern chatbots and traditional CLIs, suggesting that chatbots can be seen as descendants of CLIs, providing a natural way for users to express their needs.

Revisiting Past 'No's

• Unlocking Old Use Cases: AI can now tackle previously infeasible use cases, such as integrating documentation directly into interfaces where developers work, reducing the need for constant context switching.

Examples

• Corrective Actions in Context: Automate corrections for common command errors based on past behaviors and documentation.
• Onboarding and Education: Use AI to assist new developers by providing guidance and recommendations directly within the command line.
• Next Steps in Workflows: AI can suggest subsequent commands based on past actions, documentation, and popular use cases, improving efficiency.
• Automating Repetitive Tasks: Automate tasks like updating dependencies or injecting code changes, reducing manual effort and improving accuracy.

Focusing on Foundations

• Consistency in Patterns: Maintain and adapt well-known patterns, like flag structures in CLIs, to ensure familiarity and ease of use in new AI-driven interfaces.
• Encouraging Innovation: Keep platforms open and encourage user-driven innovation, leveraging the collective expertise of the developer community to enhance products.
• Accessible Documentation: Structure documentation to be easily consumable by AI, facilitating better training and usage of models, and contributing to a global knowledge base.

Final Thoughts

• Reflect on Past Successes: Balance innovation with proven principles to ensure new technologies enhance, rather than replace, successful existing systems.
• Collaborative Approach: Foster a culture of knowledge sharing and embrace mistakes as part of the innovation process, particularly in early stages of new technologies.
   

Meaghan encourages developers to revisit past ideas and focus on solid foundations to effectively integrate AI into their products, ultimately aiming to build better, more intuitive developer tools.