ModelRefs / Learn AI: Roadmaps, Tutorials & Concepts (2026)
Learn AI: Roadmaps, Tutorials & Concepts (2026)
The ModelRefs Learn AI hub: structured roadmaps from beginner to advanced, tutorials on prompting, agents, RAG, fine-tuning, and the core concepts behind modern AI.
Overview
ModelRefs' Learn AI hub introduces core AI concepts — prompting, Retrieval-Augmented Generation, fine-tuning, agents, evaluation — as structured roadmaps that connect directly to the model, workflow, and benchmark references those concepts inform.
Use this hub to build foundational understanding before evaluating specific models or workflows, and to identify which concept pages explain the trade-offs and boundary conditions behind a claim you're trying to assess elsewhere on ModelRefs.
These are educational references, not implementation guarantees. Understanding a concept's boundary — what it does not establish — matters as much as its benefits when interpreting vendor claims, benchmark results, or workflow maturity labels you encounter.
All explainers (15)
Every published plain-language explainer.
- HOW TO Build AI Agents
- HOW TO USE Ollama
- Test Driven Prompt Engineering Dspy
- What ARE Embeddings
- What IS A Benchmark
- What IS A Token IN AI
- What IS A Vector Database
- What IS AN AI Agent
- What IS Fine Tuning
- What IS LLM Hallucination
- What IS Model Context Window
- What IS Prompt Engineering
- What IS Prompt Injection
- What IS RAG
- What IS Tool Calling
Continue your research
Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Learn AI: Roadmaps, Tutorials & Concepts (2026).
Frequently asked questions
Where should a complete beginner start with AI?
Start with prompt engineering and a single frontier model (GPT-5 or Claude). Build fluency by prompting daily for a week before adding tools, retrieval, or agents.
Do I need to know math to learn AI?
To use AI: no. To fine-tune or design new models: yes — linear algebra, probability, and calculus. Most application work today is engineering, not research.
Should I learn to code to work with LLMs?
It helps enormously. Python plus a single SDK (OpenAI, Anthropic, or Ollama) is enough to build 90% of production AI applications.
How long does it take to be productive with AI?
Most engineers reach real productivity in two to four weeks of daily use — prompting, then tool calls, then a simple agent.
Is fine-tuning still worth learning?
Yes, but mostly for narrow tasks where prompts hit a quality ceiling. For most use cases, RAG and prompt engineering get you 95% of the way there.