Why learn LLMs & RAG?
Grounding generative models in real, current knowledge
An LLM's knowledge is frozen at training time and it has no idea about your company's documents, tickets, or database rows — and it can't just be asked to memorize all of that in a prompt. Retrieval-Augmented Generation (RAG) solves this by fetching relevant information from an external knowledge base at query time and feeding it to the model as context, which is why RAG has become the default pattern for building chatbots, search assistants, and internal tools on top of an LLM.
This is one of the fastest-growing skillsets in software right now: "AI Engineer" roles increasingly expect people who can go beyond calling a chat API and actually design a retrieval pipeline — chunking strategy, embeddings, a vector store, reranking, and an evaluation loop that proves the system isn't hallucinating.
You don't need a machine learning PhD to get productive here. Solid software engineering fundamentals plus an understanding of how embeddings, vector search, and prompting work will take you most of the way; deeper topics like fine-tuning and agentic RAG build on that foundation once the basics are second nature.
The LLMs & RAG Roadmap
Work through these in order — each step builds on the last
LLM Foundations
Understand what's actually happening inside the model you're building on top of.
Prompt Engineering
Get reliable, structured behaviour out of a model before you touch any infrastructure.
Working with LLM APIs
Build the plumbing to call a model reliably, at reasonable cost, in a real app.
Embeddings & Semantic Search
Turn text into vectors so meaning, not just keywords, can be searched.
Vector Databases
Store and search millions of embeddings fast enough for a live request.
RAG Architecture & Pipeline
Wire retrieval and generation together into a working system.
RAG Frameworks
Skip the boilerplate — loaders, chunkers, and retrieval chains, pre-built.
Agentic & Advanced RAG
Let the model decide what to retrieve, iterate, and take action — not just answer once.
Fine-Tuning & Adaptation
Know when to change model behaviour instead of, or alongside, retrieval.
Evaluation & Safety
Prove the system is accurate and grounded before it reaches real users.
Deployment & LLMOps
Serve your RAG app, keep it fast and affordable, and watch it in production.
GitHub Projects
Real, buildable projects to put on your own GitHub
Chat-with-Your-Docs App
Chunk and embed a set of PDFs or markdown docs, store them in Chroma or FAISS, and build a chatbot that answers questions with citations.
Multi-Hop Research Agent
Build an agent that decomposes a complex question into sub-queries, retrieves for each, and synthesizes a final grounded answer.
Fine-Tuned Domain Model
Fine-tune a small open-source model with LoRA on a domain-specific dataset, then compare its answers against a RAG-only baseline.
RAG Evaluation Harness
Build a test set of questions and expected answers, then score an existing RAG pipeline on faithfulness and relevance with RAGAS.
Frequently Asked Questions
Common questions from people starting out with LLMs & RAG
Do I need a machine learning background to learn RAG?
Not really. Building a RAG system is mostly software engineering — calling APIs, chunking text, storing vectors, and writing retrieval logic. A conceptual grasp of embeddings and how a transformer processes context is enough; you don't need to train models from scratch.
RAG vs fine-tuning — which should I use?
RAG is generally the better default for adding knowledge — it's cheaper, updates instantly when your data changes, and lets you cite sources. Fine-tuning is better for changing how a model behaves (tone, format, a narrow skill) rather than what it knows. Many production systems combine both.
Is RAG still needed now that context windows are so large?
Long context helps but doesn't replace RAG for most real applications — stuffing millions of tokens into every request is slow and expensive, and models still attend unevenly across very long contexts. Retrieval keeps the prompt focused on what's actually relevant, which tends to be both cheaper and more accurate.
LangChain vs LlamaIndex — which framework should I learn?
LlamaIndex is more focused on data ingestion and retrieval, which makes it a natural fit for RAG-first apps. LangChain is broader — chains, agents, and integrations across the whole LLM app lifecycle. Many teams use both, or write the pipeline directly against a vector database once they understand the underlying pattern.
Which vector database should I start with?
FAISS or Chroma are the easiest to start with locally since they need no separate service to run. Move to a managed option like Pinecone, or a self-hosted one like Weaviate or Milvus, once you need production-scale indexing, filtering, or multi-tenant isolation.
How do I prepare for an AI/LLM engineer interview?
Be ready to explain the full RAG pipeline end to end — chunking choices, why you picked a given embedding model, how reranking improves precision, and how you'd evaluate whether the system is hallucinating. Having a project with a real evaluation harness (not just a working demo) stands out.
Track complete
From understanding what a transformer does to shipping an evaluated, production RAG pipeline — that's the core of what's expected from an AI engineer today. Keep building, and pair it with backend or MLOps skills to round out a full production-AI profile.
Where next?
Keep exploring by domain or drill into a single skill