Heard of Gemma and some other model names but don’t know much about them
Tried Ollama once - installed a model via terminal, asked one question, then quit
Understand basic concepts like prompts, seeds, and temperature settings for customizing AI responses
I want to become proficient with large language models and related technologies like RAG implementations and Langchain development. The field seems massive and I feel completely lost about where to begin my journey.
What learning sequence would experienced practitioners recommend? I’m looking for a structured approach that takes me from my current beginner level to someone who can work confidently with these AI technologies. Any suggested resources or milestone goals would be incredibly helpful.
Begin with the fundamentals; I made the mistake of diving straight into Langchain without grasping the basics, which led to confusion. Start experimenting with local models using Ollama, exploring different types and sizes to understand their capabilities. This hands-on approach is invaluable. Once you’re familiar with model interactions, deepen your knowledge of embeddings and vector databases, key components of RAG systems. Construct a simple RAG pipeline manually before exploring frameworks to appreciate their design choices. In my experience, mastering prompt engineering first, followed by tokenization, context windows, and creating a basic RAG from scratch with vector storage, prepares you well before introducing frameworks, ensuring each concept builds on a solid understanding.
Start with data flow - it’s everything. Most people jump straight into frameworks without understanding how information actually moves through these systems. I wasted weeks debugging RAG implementations because I didn’t get chunking strategies and retrieval mechanisms. Play around with embeddings using sentence transformers locally first. Load text files, create embeddings, query them manually before touching any framework. This saved me tons of time later when production systems went sideways. With LangChain, ignore the hype initially. Use it as a convenience wrapper once you know what’s happening underneath. Their abstraction layers are confusing when you’re learning, but great for quick prototyping later. My breakthrough came from building a terrible RAG system that barely worked, then making it better piece by piece. You’ll hit real problems like context bleeding, bad retrievals, and hallucinations that tutorials don’t cover.
Don’t build RAG systems from scratch - automate everything instead. I’ve watched too many engineers waste months on manual setups when they could be solving real problems.
Just start building. Grab a simple use case like team document Q&A or your personal knowledge base. You’ll learn way more from actual problems than theoretical tutorials.
Stop comparing every vector database and framework out there. Pick one, ship something that works, then worry about optimization. The real learning happens when you hit edge cases anyway.
Automate your entire pipeline from day one - that’s the biggest time saver. Don’t manually manage model deployments, data preprocessing, or API integrations. Set up proper automation so you can iterate fast and focus on the AI logic instead of infrastructure headaches.
I’ve helped teams go from zero to production LLM apps in weeks this way. Treat it like any software project: automate everything and ship working solutions fast.
pick one project and stick with it until u ship something. I wasted tons of time reading tutorials instead of actually building stuff. try making a chatbot with your own data - that’s what finally made embeddings and vector search click for me. LangChain’s docs are messy, but the quickstart’s decent once you’ve got Ollama running locally.