What's the Best AI Framework
to Learn?

Start with LangChain or LlamaIndex - they have the largest communities and most job postings in 2026. But here's the truth: the specific framework matters less than understanding the patterns. Learn how RAG pipelines work, how agents make decisions, how to evaluate outputs. With those fundamentals solid, switching frameworks takes days, not months. I help coaching clients build this foundation so they're never locked into one tool.

LangChain excels at complex agent workflows and has broader integrations. LlamaIndex is stronger for RAG and document-heavy applications. But honestly? Most production systems use pieces of both, or neither - they use the OpenAI SDK directly. The 'best' choice depends on your use case. In coaching, we identify what you're actually building and match the tool to the job.

It probably will - and that's fine. Framework-specific knowledge has a half-life of 18-24 months. Concept knowledge (prompt engineering, retrieval strategies, agent architectures) lasts for years. That's why I focus on teaching transferable skills. When LangChain 2.0 dropped breaking changes, my clients adapted in a weekend because they understood the underlying patterns.

For most AI engineering roles: one or two deeply, plus awareness of others. Employers care more about your ability to build working systems than your framework collection. Deep knowledge of LangChain plus basic familiarity with alternatives beats surface-level knowledge of five frameworks. In coaching, we go deep on one framework while building transferable skills.

Based on 2026 job postings: LangChain leads, followed by LlamaIndex and Semantic Kernel (especially for Microsoft shops). But here's the pattern I see: job listings ask for specific frameworks, but interviews test problem-solving. Can you design a RAG system? Debug a hallucinating agent? That's what lands offers. Framework syntax is googleable; architectural thinking isn't.

Yes, especially if you have strong Python fundamentals and can demonstrate AI concepts through projects. Many companies are willing to train on their specific stack. What they can't easily train: problem decomposition, debugging complex AI systems, and the judgment to know when AI is the wrong solution. Those skills come from building, and coaching accelerates that dramatically.

Not necessarily. While some programming experience is helpful, many of my clients have successfully transitioned from web development, data science, or other technical backgrounds. We'll assess your current skills during our strategy call and create a personalized plan that meets you where you are.

Most clients invest 10-15 hours per week, but this can be flexible based on your schedule. We'll have weekly 1:1 calls plus time for you to work on projects and learning. The key is consistency. Regular, focused effort beats occasional marathons.

You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.

Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.

Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.

Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.