Weekend read: My take on a great roadmap for becoming an “AI Generalist”—and why it matters for DevSecOps, Security, Cloud, and App teams.
I came across an insightful article recently about the rise of AI Generalists—people who know enough across AI domains to build tools, solve problems, and adapt fast. It got me thinking about how this mindset fits perfectly with what we do in DevSecOps and related teams.
A few things stood out:
🚀 AI fluency for all teams: Whether you’re in app development, security, cloud, or DevOps, understanding AI fundamentals like large language models and prompt engineering is key to unlocking new automation and innovation opportunities. It’s about speaking the same language as the tools we rely on.
✔️ AI-driven workflows are game changers: Moving beyond manual processes, building AI-powered workflows accelerates delivery, strengthens security measures, and helps reduce operational risk by automating repetitive tasks and surfacing insights faster.
🔍 Smarter monitoring and response: AI-powered tools can transform how we detect issues, investigate incidents, and manage cloud resources—making responses faster, more accurate, and less resource-intensive.
➡️ Leadership’s critical role: To truly benefit, leaders must champion AI adoption by embedding AI skills across teams, fostering a culture that embraces continuous learning and innovation, and making AI a strategic priority for the organization.
The AI Generalist Journey
Level 1: Foundations of AI & LLMs
Build a solid mental model of modern AI—understanding models like ChatGPT and Claude, core concepts like tokens and reasoning limits, plus basic prompting techniques.
Tools: ChatGPT, Claude, Perplexity, CustomGPTs
Level 2: Prompt Engineering & Tuning
Move from casual use to structured results by mastering prompt frameworks, retrieval-augmented generation (RAG), API calls, and fine-tuning.
Tools: LangChain, OpenAI Playground, Pinecone, VectorShift
Level 3: Image, Audio & Video Generation
Expand beyond text by generating visuals, audio, and video—perfect for content creation and automation.
Tools: Midjourney, RunwayML, ElevenLabs, Descript
Level 4: Automations & AI Agents
Build micro-products and connect AI to real-world systems with no-code and workflow automation.
Tools: Zapier, Make, n8n, Replit
Level 5: Build AI-Powered Products
Design and lead full-stack AI solutions, stay updated on open-source vs proprietary models, and drive team-wide AI adoption.
Tools: Ollama, Supabase, HuggingFace, Vercel
This isn’t just hype—AI is shaping every part of software delivery and security. So the big question is: Are we ready to lead this change, or will we be left playing catch-up?
Full disclosure: I used ChatGPT to help format this post from my personal notes—because why not use AI to talk about AI? 🙂
Enjoy your weekend—and maybe give this some thought!
Originally posted on LinkedIn