Title: GenAI Syllabus - Google Reichman AI Tech School

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*Changes may apply
# GenAI & LLMs Applications Development Syllabus

![Image 5](https://www.graitechschool.co.il/wp-content/uploads/2022/12/Mask-Group-9042.png)

![Image 6](https://www.graitechschool.co.il/wp-content/uploads/2022/12/Gilad-Kavalerchik__221220_130.jpg)

Module 1: Fundamentals of Generative AI 
*   **Introduction to GenAI:**History, evolution, and core concepts (Generative vs. Discriminative AI).
*   **Market Landscape:**Overview of leading tools and platforms.
*   **LLM Architecture:**Deep dive into the Transformer architecture, tokenization, and embeddings.
*   **Model Types:**Understanding the differences between open-weight and closed-weight models.

![Image 7](https://www.graitechschool.co.il/wp-content/uploads/2022/12/AI-2.jpg)

Module 2: Prompt/Context Engineering & Text Applications 
*   **Working with LLMs APIs:**including Google Gemini, OpenAI, Anthropic as well as open-source models from HuggingFace.
*   **Prompt/Context Engineering Principles:**From zero-shot and few-shot to advanced prompting techniques like chain of thought (CoT) and context handling.
*   **Understanding the Parameters:** all the settings you can tweak to get the most of your LLMs.
*   **Evaluation driven development:** how to evaluate GenAI products and what is the correct process?
*   ****Reasoning:** The evolution of reasoning, from simple CoT to today’s reasoning models**
*   **Company Brain:** large scale context management 

![Image 8](https://www.graitechschool.co.il/wp-content/uploads/2022/12/%D7%92%D7%9C-%D7%95%D7%90%D7%93%D7%99%D7%A1%D7%9C%D7%90%D7%9D-scaled.jpg)

Module 3: Retrieval Augmented Generation (RAG)
*   **RAG fundamentals:** The need for external memory, vector databases, and semantic search.
*   **Basic pipelines:** Building a standard RAG system.
*   **Advanced RAG:** Re-ranking, hybrid Search, and handling complex documents.
*   **Evaluation of RAG systems:** Metrics and methods for evaluating RAG performance.

![Image 9](https://www.graitechschool.co.il/wp-content/uploads/2022/12/%D7%90%D7%95%D7%A8%D7%94-scaled.jpg)

Module 4: Fine-tuning
*   **Fine-tuning:** what does it mean?
*   **Parameter efficient fine-tuning:** including LoRA.
*   **Peeking inside the model:** An introduction to mechanistic interpretability, understanding how a model works internally, from features and circuits to what individual neurons actually represent.
*   **Understanding fine-tuning process of reasoning models:** We will cover the underlying theory and conceptual foundations, without engaging in large-scale fine-tuning in practice.

![Image 10](https://www.graitechschool.co.il/wp-content/uploads/2022/12/Gilad-Kavalerchik__221220_20-e1673086560324.jpg)

Module 5: AI Systems 
*   **Workflows & Systems:**what is an AI system and why RAG is just a specific case of it? Reviewing common architectures.
*   **Automation tools:**such as n8n.
*   **ADLC:** Coding better with AI – Cursor, Claude, TDD, rules, and a full development harness
*   **LLM cost management:** offline and online
*   **Local model hosting**
*   **Harness engineering:** Skills, rules, model routing, real-time LLM decision making

![Image 11](https://www.graitechschool.co.il/wp-content/uploads/2022/12/Gilad-Kavalerchik__221220_103-e1671960069347.jpg)

Module 6: AI Agents 
*   **Development AI Frameworks:** Introduction to LangChain, LangGraph and LlamaIndex.
*   **Tool use:** Function calling and connecting LLMs to external APIs.
*   **MCP & other protocols (such as A2A):** Tool use 2.0.
*   **AI agents:** Differences between chatbots and agents (e.g., ReAct design pattern).
*   **Introduction to Google ADK.**
*   **Agents evaluation.**

![Image 12](https://www.graitechschool.co.il/wp-content/uploads/2023/07/%D7%A2%D7%99%D7%A0%D7%AA-%D7%95%D7%93%D7%A0%D7%99%D7%90%D7%9C-2-scaled.jpg)

Module 7: Final Project
*   **Ideation & Planning:** Selecting a business problem and defining the solution architecture.
*   **Development:** Hands-on implementation of a GenAI-based product.
*   **Demo Day:** Final presentation of the projects.

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