BeginnerStart here. No prior knowledge assumed.
0 of 5 complete
Lesson 1
How LLMs Work — Plain English
Tokens, training, temperature, context windows — the core concepts explained without maths.
Lesson 2
What is RAG?
Retrieval-Augmented Generation — what it is, why it exists, and how it solves LLM limitations.
Lesson 3
RAG vs Fine-Tuning vs Prompting
When to use each approach — the decision tree for AI implementation without building from scratch.
Lesson 4
Fine-Tuning Explained
What fine-tuning does, when it is worth the cost and complexity, and what it cannot achieve.
Lesson 5
Context Windows and Limitations
What context windows are, why they matter, and how they affect what you can build.
What is RAG?
What is a token in LLM context?
IntermediateBuild on the basics.
0 of 5 complete
Lesson 1
Embeddings and Vector Search
How text becomes numbers and why it enables semantic search — the concept behind RAG retrieval.
Lesson 2
LLM APIs — What They Offer
OpenAI API, Anthropic API, Google API — what they provide and the key parameters that affect outputs.
Lesson 3
Prompt Templates and System Prompts
How structured prompts and system prompts configure LLM behaviour — the engineering layer.
Lesson 4
Evaluation and Benchmarking
How to assess LLM outputs — the metrics teams use and the limitations of automated evaluation.
Lesson 5
Open Source vs Closed Models
Llama, Mistral, and the open-source LLM landscape versus GPT-4 and Claude — trade-offs and use cases.
When is fine-tuning preferred over RAG?
What is an embedding in AI?
AdvancedDepth, edge cases and strategy.
0 of 5 complete
Lesson 1
Building a Simple RAG System
The conceptual architecture of RAG — document ingestion, embedding, retrieval, and generation.
Lesson 2
LLM Cost and Efficiency
Tokens, pricing, and how to build cost-efficient AI systems — the economics of LLM deployment.
Lesson 3
AI Hallucination — Technical Perspective
Why hallucination happens at a technical level and the mitigation approaches that reduce it.
Lesson 4
Multimodal LLMs
Models that handle text, images, audio, and video — how multimodal capability works and what it enables.
Lesson 5
Talking to Engineers About AI
The vocabulary and mental models that help non-technical people collaborate effectively on AI projects.
What is the primary limitation of RAG?
What is temperature in LLM context?