Learn LLM Path: the gap-aware roadmap from LLM fundamentals to agentic AI
Learn LLM Path is a free roadmap to master large language models properly, step by step. Instead of juggling forty open tabs, you follow one curated path of ten pillars that runs from LLM fundamentals and transformer architecture through RAG, agents and the Model Context Protocol to evaluation-driven development and security. Every resource is hand-picked, time-estimated and quizzed, so you always know what to study next, why it matters, and how much of the journey remains. Whether you are starting your first LLM course or sharpening skills you already use at work, the path adapts and your progress persists.
Why most roadmaps leave gaps
Most generative AI curricula share the same blind spots. They mention prompt injection once and skip real prompt injection prevention, ignoring the other nine OWASP Top 10 for LLM risks entirely. They demo toy retrieval and skip hybrid search, reranking and evaluation, which is exactly where production systems break. They mention tool calling once and call agents solved. Learn LLM Path was designed against those gaps.
Pillar 9: Evals & Observability treats LLM evals as core engineering: golden datasets, LLM observability with tracing and error analysis, regression gates and LLM-as-a-judge patterns you can run in CI. Pillar 10: Security walks all ten OWASP Top 10 for LLM risks hands-on, from system-prompt leakage to MCP tool poisoning, with mitigations you can actually demo. The same logic runs through every module: agentic RAG instead of toy retrieval, context engineering instead of vibes, and a 70% quiz gate after each pillar so gaps cannot quietly follow you forward.
How the roadmap works: plan, track, review, repeat
This is a practical roadmap to learning large language models, not another passive LLM course. It starts with the plan builder: tell it how many hours per week you have and it distributes every remaining resource across dated weeks, then exports the whole schedule to your calendar as an .ics file.
Then work the path. Each item links to one specific video, article, doc or repo with an honest time estimate, so an evening session has a defined shape. Every pillar ends with a quiz; score at least 70% and the next pillar unlocks for tracking. Missed questions do not disappear: they become spaced-repetition review cards that resurface tomorrow, then two, four, eight and sixteen days later until they stick.
Along the way you get private notes on any resource, community votes that surface what actually helps, day streaks, and a leaderboard if you want company. Three tracks fit different budgets: Short & Concise is the default, Deeper adds job-ready depth, and the free track swaps paid courses for free alternatives wherever they exist so you can learn agentic AI free from first video to final project. If a passive course ever left you with notes and no skills, this structure is the fix: small units, real checkpoints, visible progress. It is the simplest honest way to learn LLM free of charge and free of filler.
What you will learn across the ten pillars
The curriculum reads like the table of contents of a serious AI engineer roadmap, assembled from the best resources to learn LLM concepts, the best resources for large language models in production, and the best resources for agentic AI specifically. Every concept appears exactly where it belongs in the dependency order, so each idea compounds on the last.
Transformer & LLM Internals
explain how an LLM works end-to-end without deep math. 3Blue1Brown chapters are the visual gold standard - watch in order
LLM App Fundamentals
go from "I can call an API" to "I can ship an LLM feature." Ed Donner's course (Ollama-first, laptop-friendly) is strongest here
RAG (Basic → Advanced → Eval)
build RAG that works in production. Biggest hidden gap - basic tutorials stop at "embed + top-k," real systems need hybrid search, reranking, evaluation
Fine-Tuning (LoRA/QLoRA)
know when NOT to fine-tune, and how LoRA/QLoRA make it cheap. Optional for most app work - but a common interview topic
Agents & Design Patterns
understand agents from first principles (vendor-neutral) before frameworks. Andrew Ng's course is the backbone
Context Engineering & Memory
the #1 skill for reliable agents in 2025-26, and almost always missing from course lists
Frameworks
fluency in the tools that implement the patterns. Fix vocabulary first, then go deep on LangGraph (the one interviewers ask about most)
Model Context Protocol (MCP)
understand, build, AND secure MCP. Intro videos cover why/architecture but skip client-side primitives and the entire MCP security attack class
Evals & Observability
stop guess-and-tweak. Evaluation-driven development is the single biggest predictor of agent-building success (per Andrew Ng)
Security (Full OWASP Top-10 for LLM)
know all ten risks, not just prompt injection. This is the biggest single-video gap - one injection video leaves 9 risks uncovered
In order: transformer architecture explained visually with attention, tokenization and forward passes (P1), then prompt engineering fundamentals, structured outputs and the tool calling and function calling LLM apps depend on in production (P2). Retrieval comes next: embeddings, hybrid BM25 plus dense search, reranking, GraphRAG and agentic RAG evaluated with RAGAS (P3), followed by LoRA and QLoRA fine-tuning, including when not to bother (P4).
The agent half of the path is a complete AI agent development roadmap: ReAct, reflection and planning patterns with multi-agent systems tutorial builds (P5), context engineering and memory (P6), frameworks such as LangChain and LangGraph compared honestly against raw SDKs (P7), and the Model Context Protocol, where you build, connect and secure MCP servers and clients (P8). P9 and P10 close the loop with evals, observability and the LLM security risks that real audits probe for, as described above.
Who this roadmap is for
Three kinds of learners get the most out of it. Beginners with basic Python who want to learn large language models seriously, without enrolling in anything. Career switchers who need an honest LLM engineer roadmap rather than hype threads. And working developers already shipping generative AI features who feel the distance between chatbot demos and reliable agents widening; for them it doubles as one of the few generative AI and LLM learning paths that goes all the way to production concerns.
On pacing, be skeptical of the How I Studied LLMs in Two Weeks genre. You can genuinely cover fundamentals in two focused weekends; working competence takes about seventeen weeks at one to one and a half hours on weekdays, faster full-time. As an agentic AI roadmap for 2026 it stays current too: resources are re-verified by automated link checks, and the deeper editions add deployment guides and capstone portfolio projects.
Frequently asked questions
Is Learn LLM Path really free?
Yes. Browse every pillar and every resource without an account. A free account adds progress tracking, private notes and quizzes, and a dedicated free track swaps paid courses for free alternatives wherever they exist.
Do I need math or machine learning background?
No. The path assumes basic Python and curiosity. Like the engineering roadmaps it draws from, it teaches just enough neural-network intuition to reason about systems, never derivations.
How long does the roadmap take?
Roughly 17 weeks at 1 to 1.5 hours on weekdays plus weekend project time, following the suggested sequence. A full-time pace compresses that by about 30%.
What makes this different from other AI roadmaps?
Gap-aware coverage. Most curricula stop at prompt injection and skip the other OWASP Top 10 for LLM risks, or skip evaluation entirely. Here, evals, observability and security are first-class pillars gated by quizzes, and spaced review resurfaces anything you miss until it sticks.