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@@ -93,7 +93,7 @@ Throughout this course you'll find many code examples and exercises, so we encou
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| 3 |[Prompt engineering](/lessons/03-prompt-engineering/README.md)| Learn Prompt engineering fundamentals, techniques, and meta-prompts for better AI outcomes. |
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| 4 |[Structured output](/lessons/04-structured-output/README.md)| Learn structured output, how to extract data from prompts, and present it in various formats, such as JSON, for easier consumption. |
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| 5 |[Retrieval augmented generation (RAG)](/lessons/05-rag/README.md)| Learn the basics of RAG, how to integrate external data, and how to leverage it for more relevant, accurate AI responses. |
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| 6 |[Tool calling/Function calling](/lessons/06-tool-calling/README.md)| Learn how to give your LLM extra capbilities, bring your own functions |
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| 6 |[Tool calling/Function calling](/lessons/06-tool-calling/README.md)| Learn how to give your LLM extra capabilities, bring your own functions |
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| 7 |[MCP, Model Context Protocol ](/lessons/07-mcp/README.md)| Teaches how to get started with MCP to standardize how to expose prompts, resources and tools |
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| 8 |[Enhancing MCP Clients with Large Language Models](/lessons/08-mcp-advanced/README.md)| Learn how to improve your MCP app by improving clients with LLM and more |
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