LangChain vs LlamaIndex
Which Should You Learn?

It depends on what you're building. If your primary goal is retrieval-augmented generation (RAG) with complex data sources, start with LlamaIndex. It's purpose-built for indexing, retrieval, and querying data. If you're building AI agents, multi-step workflows, or applications that need to use multiple tools, start with LangChain. Most developers eventually learn both, so the first choice matters less than just starting.

For most AI engineering roles in 2026, understanding both is valuable but mastery of one is sufficient to start. The core concepts (embeddings, vector stores, retrieval, prompting, chains) transfer directly between frameworks. Once you deeply understand one, picking up the other takes days, not months. Focus on building real projects rather than collecting framework knowledge.

Job postings mention LangChain more frequently, but employers really want developers who can build working AI applications. Framework-specific knowledge is easy to acquire; problem-solving ability and production experience are not. A portfolio project using either framework demonstrates more value than surface-level knowledge of both.

This fear is overblown. Both frameworks share similar architectural patterns and abstractions. Developers who deeply understand LangChain can become productive in LlamaIndex within a week, and vice versa. The mental models, debugging skills, and production experience you gain are framework-agnostic. You're not locked in.

Yes. Haystack, Semantic Kernel, and raw API calls with custom code are all viable. Some teams build custom solutions for more control. However, LangChain and LlamaIndex have the largest communities, most tutorials, and best integration ecosystems. For learning and getting hired, they remain the practical choices in 2026.

A coach who works with AI tools daily can assess your goals in one conversation and recommend the right starting point. More importantly, coaching provides structured learning with accountability, code review on real projects, and guidance past common pitfalls. What takes months of solo trial-and-error often takes weeks with expert guidance.

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.