Meta Llama AI Certification

Lessons

1. 1LESSON 1 — META LLAMA FUNDAMENTALSDifficulty: Beginner\ Estimated Time: 3035 minutes Learning Objectives Students will learn: What Meta Llama is. What a large language model is. What openweight means. How Llama dif3m
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2. 2LESSON 2 — LLAMA MODELS & LLAMA 4Difficulty: Beginner → Intermediate\ Estimated Time: 40 minutes Learning Objectives Students will learn: Llama model generations. Llama 4. Llama 4 Scout. Llama 4 Maverick. Model se3m
3. 3LESSON 3 — LLAMA PROMPT ENGINEERINGDifficulty: Intermediate\ Estimated Time: 3540 minutes Learning Objectives Students will learn: Prompt structure. System instructions. Context. Constraints. Output formatting. Exam3m
4. 4LESSON 4 — LLAMA MULTIMODAL AIDifficulty: Intermediate\ Estimated Time: 4045 minutes Learning Objectives Students will learn: What multimodal AI means. Llama 4 vision capabilities. Image understanding. Visual q3m
5. 5LESSON 5 — LLAMA LONG CONTEXT & DOCUMENT INTELLIGENCEDifficulty: Intermediate → Advanced\ Estimated Time: 45 minutes Learning Objectives Students will learn: Context windows. Longdocument analysis. Multidocument analysis. Information3m
6. 6LESSON 6 — LLAMA FOR CODING & SOFTWARE DEVELOPMENTDifficulty: Advanced\ Estimated Time: 4550 minutes Learning Objectives Students will learn: Code generation. Debugging. Refactoring. Code review. Testing. Documentation. Architectu3m
7. 7LESSON 7 — LLAMA API & APPLICATION DEVELOPMENTDifficulty: Advanced\ Estimated Time: 4550 minutes Learning Objectives Students will learn: What an AI API is. How applications communicate with Llama. Llama API concepts. Authenti3m
8. 8LESSON 8 — LLAMA RAG, EMBEDDINGS & KNOWLEDGE SYSTEMSDifficulty: Advanced\ Estimated Time: 4550 minutes Learning Objectives Students will learn: What RAG is. Why RAG is useful. Retrieval. Embeddings. Vector databases. Context injecti3m
9. 9LESSON 9 — LLAMA FINE-TUNING & MODEL CUSTOMIZATIONDifficulty: Advanced\ Estimated Time: 45 minutes Learning Objectives Students will learn: What finetuning is. When finetuning is appropriate. Training data. Dataset quality. Evalua3m
10. 10LESSON 10 — LLAMA TOOLS, FUNCTION CALLING & AI AGENTSDifficulty: Advanced\ Estimated Time: 4550 minutes Learning Objectives Students will learn: Tool calling. Function calling. AI agents. Multistep workflows. Planning. External syste3m
11. 11LESSON 11 — LLAMA SECURITY, SAFETY & RESPONSIBLE AIDifficulty: Advanced\ Estimated Time: 45 minutes Learning Objectives Students will learn: Model safety. Prompt injection. Jailbreaks. Data leakage. Model misuse. Llama Guard. Secur3m
12. 12LESSON 12 — META LLAMA MASTERY CAPSTONE & CERTIFICATIONDifficulty: Advanced\ Estimated Time: 6075 minutes This is the final Meta Llama certification lesson. Students must demonstrate practical mastery of the Llama ecosystem.3m
13. 13CAPSTONE OPTIONSChoose ONE: OPTION A LLAMA APPLICATION Build a Llamapowered application. OPTION B RAG SYSTEM Create a Llama knowledgebase assistant. OPTION C CODING ASSISTANT Create a Llamapowered3m
14. 14CAPSTONE REQUIREMENTSThe student must submit: 1. Problem Statement What problem are you solving? 2. Objective What should Llama accomplish? 3. Users Who will use the system? 4. Model Selection Explain 3m
15. 15CAPSTONE SCORINGTotal: 100 points Llama Knowledge 20 points Architecture 20 points Prompt Engineering 15 points Practical Implementation 15 points Evaluation 10 points Security 10 points Responsib3m
16. 16FINAL META LLAMA EXAMINATIONAfter completing the capstone, students must complete a 20question final examination. Passing Score: 80% The exam must cover: Llama fundamentals Openweight models Llama 4 Scout Mav5m