It’s simple to get an LLM to answer a question in a Jupyter Notebook. But making that system work reliably, affordably, and safely for 10,000 users is the real challenge. That’s where LLMOps comes in. If you’re a student or want to become an AI engineer, this is your chance. While others are still adjusting prompts, LLMOps engineers are building the systems that keep AI running. In this article, I’ll share a full learning path for LLMOps in 2026.
LLMOps Learning Path
Here’s a step-by-step learning path to help you master LLMOps in 2026.
Step 1: Python & The API First Mindset
Before working with neural networks, it’s important to focus on the basics. In 2026, you probably won’t train models from scratch. Instead, you’ll be orchestrating them.
A raw LLM, such as GPT-5 or Claude 3.5, is like a powerful engine without controls. It has a lot of potential, but you need to guide it.
You’ll need solid Python skills, especially for working with APIs (FastAPI is the standard) and containerization tools like Docker.
Begin by creating a simple wrapper. Instead of only using the OpenAI SDK in a script, try building a FastAPI endpoint that takes a user request, sends it to the LLM, and returns the response.
Here are some resources you can follow:
Step 2: RAG is the New Database
In 2026, hallucination is no longer just a quirk; it’s considered a bug. To address this, we use RAG (Retrieval-Augmented Generation).
LLMOps for RAG involves more than just the model; it’s also about the library, or vector database. You’ll need to learn how to break data into small parts (chunking), turn them into numbers (embeddings), and store them in a vector database like Pinecone, Weaviate, or Chroma.
Industry projects now require advanced RAG. Simply retrieving documents isn’t enough. You’ll also need to use re-ranking, which checks if the information you found is actually relevant, and hybrid search, which combines keywords with vector math.
Here are some resources you can follow:
Step 3: The Age of Agents
This is the biggest change for 2026. We’ve moved from chatbots to agents.
A chatbot only talks, but an agent takes action. Imagine a chatbot as a receptionist who answers questions, while an agent is like an executive assistant who can answer the phone, check your calendar, book flights, and send emails.
You’ll need to learn orchestration frameworks such as LangGraph or CrewAI.
Here are some resources you can follow:
Step 4: Evaluation & Observability
This is where the operations side of LLMOps is most important. How can you tell if your bot is behaving appropriately?
In traditional software, answers are straightforward; 2 plus 2 always equals 4. In AI, a question like “Hello” could get responses such as “Hi,” “Greetings,” or “What do you want?” All are possible, but some are better than others.
Now, we use advanced LLMs to evaluate less capable ones. It’s similar to a senior professor grading a teaching assistant’s papers. You’ll need tools like Arize Phoenix, LangSmith, or Weights & Biases to understand what’s happening inside the process.
Here are some resources you can follow:
Closing Thoughts
This is the full learning path you can follow for LLMOps in 2026.
Traditional coding is like building with bricks; they stay where you put them. LLMOps is more like gardening: you plant the model, feed it with data, and shape it through evaluation.
If you found this article helpful, you can follow me on Instagram for daily AI tips and practical resources. You may also be interested in my latest book, Hands-On GenAI, LLMs & AI Agents, a step-by-step guide to prepare you for careers in today’s AI industry.
