> For the complete documentation index, see [llms.txt](https://opencampus.gitbook.io/opencampus-machine-learning-program/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://opencampus.gitbook.io/opencampus-machine-learning-program/courses/archive/practical-engineering-with-llms/week-2-prompt-engineering.md).

# Week 2 - Prompt Engineering

#### Session Content

* Quiz
* Homework presentation
* Short recap on intro to LLMs & prompt engineering
* Breakout session on prompt hubs
* Anatomy of an app
* Breakout session on project ideas
* Presentation of project ideas and default projects
* Homework for next week

#### Session Slides

{% file src="/files/5I0Vdv76MyUbT5aE5Eec" %}

#### Todo until next session

* Watch the short course ["LangChain for LLM Application Development"](https://learn.deeplearning.ai/langchain) (\~1h 40min)
* If the videos ["Question and Answer"](https://learn.deeplearning.ai/langchain/lesson/5/question-and-answer) and ["Evaluation"](https://learn.deeplearning.ai/langchain/lesson/6/evaluation) are too confusing for you right now, as they introduce a lot of concepts in a short amount of time, you can skip these videos as this content will also be part of later sessions.
* Work through the Jupyter notebook ["HomeworkLangChain"](https://colab.research.google.com/drive/1XcJ2VyTnxxk_1fngRXcHSg6YJq39bjnm?usp=sharing) and complete the tasks at the bottom of the notebook.

{% file src="/files/55dVlLynY5w7kodDwkcg" %}

["Colab Link HomeworkLangChain"](https://colab.research.google.com/drive/1XcJ2VyTnxxk_1fngRXcHSg6YJq39bjnm?usp=sharing) (Copy the notebook to your Google Drive before working on it.)


---

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