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Adding tracker back
We have no data. We need data! Signed-off-by: JJ Asghar <[email protected]>
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docs/lab-1.5/README.md

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## Conclusion
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**Congratulations!** Now you have Open-WebUI running and it's configured to work with `granite3.1-dense` and `ollama`. Move on to [Lab 2](https://ibm.github.io/opensource-ai-workshop/lab-2/) and have a chat with your model!
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docs/lab-1/README.md

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## Conclusion
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**Congratulations!** Now you have AnythingLLM running and it's configured to work with `granite3.1-dense` and `ollama`. Move on to [Lab 2](https://ibm.github.io/opensource-ai-workshop/lab-2/) and have a chat with your model!
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**Congratulations!** Now you have AnythingLLM running and it's configured to work with `granite3.1-dense` and `ollama`. Move on to [Lab 2](https://ibm.github.io/opensource-ai-workshop/lab-2/) and have a chat with your model!
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docs/lab-2/README.md

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Spend as much time as you want asking your LLM about anything about any topic and exploring how you can alter its output to provide you with more interesting or satisfying responses.
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When you are acquainted with your model, move on to [Lab 3](https://ibm.github.io/opensource-ai-workshop/lab-3/) to learn about Prompt Engineering.
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docs/lab-3/README.md

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These prompts don't have any previous data, structure, or guidelines provided with the request. Here's an example:
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```
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I want to explore pasta making recipes.
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I want to explore pasta making recipes.
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Do you have any suggestions for recipes that are unique and challenging?
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```
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As a follow-up, I'll ask for the recipe to make the "Homemade Ravioli" option in the response I received:
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```
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I do like some homemade ravioli.
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I do like some homemade ravioli.
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What is the spinach, ricotta and cheese recipe you suggest?
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```
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Take the provided prompts, and replace the [words] in brackets with your own choices. Get creative with it!
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```
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I want you to act as a customer support assistant who is [characteristic].
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I want you to act as a customer support assistant who is [characteristic].
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How would you respond to [text] as a representative of our [type] company?
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```
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My version will be:
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```
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I want you to act as a customer support assistant who is an expert in shipping logistics.
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I want you to act as a customer support assistant who is an expert in shipping logistics.
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How would you respond to client who has had their freight lost as a representative of our company?
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```
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That's not a satisfactory or interesting response, right? We need to interate on it, and provide more context about the client, like what they may have lost. **Tip: always think about adding more context!**
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```
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The freight they lost was an industrial refrigerator, from Burbank, California to Kanas City, MO.
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I need you to write out an apology letter, with reference to the shipping order #00234273
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and the help line of 1-800-347-2845, with a discount code of OPPSWEDIDITAGAIN for 15% off
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shipping their next order. Mention that sometimes, the trucks have accidents and need
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The freight they lost was an industrial refrigerator, from Burbank, California to Kanas City, MO.
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I need you to write out an apology letter, with reference to the shipping order #00234273
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and the help line of 1-800-347-2845, with a discount code of OPPSWEDIDITAGAIN for 15% off
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shipping their next order. Mention that sometimes, the trucks have accidents and need
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to be repaired and we should be able to reach out in a couple weeks.
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## Conclusion
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Now that you know the basics of prompt engineering and simple techniques you can use to level-up your prompts, let's move on to [Lab 4](https://ibm.github.io/opensource-ai-workshop/lab-4/) and apply what you've learned with some exercises.
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docs/lab-4/README.md

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## 2. Extract Summary Topics
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**Prompt a model to extract a short, bulleted summary from a meeting transcript.**
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The goal is to turn raw dialogue into clear, actionable notes — just like you'd want after a real meeting.
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**Prompt a model to extract a short, bulleted summary from a meeting transcript.**
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The goal is to turn raw dialogue into clear, actionable notes — just like you'd want after a real meeting.
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You can use the following meeting transcripts.
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It's windy as the sun sets over the horizon, casting a warm glow on the
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landscape.
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A gentle breeze rustles the leaves of the trees as the sun begins to set
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A gentle breeze rustles the leaves of the trees as the sun begins to set
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and colour the landscape.
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```
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- Content Writer at BuzzTone Agency (1 year)
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Education:
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Education:
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- B.A. in Communications
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<details>
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<summary> Show example prompt </summary>
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```
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The following text is my resume for my career up
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until my most recent job. I am [current job] with
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[years of experience] considered a highly skilled
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individual in [core skill set]. I want to build a
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hire me for a role with both my current skill set
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Focus on providing enough detail to create a cohesive story for a one-session gameplay experience.
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Think about the environment, the objectives, challenges, and the final boss encounter—all while keeping it concise and exciting for the players.
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## Conclusion
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Well done! By completing these exercises, you're well on your way to being a prompt expert. In [Lab 5](https://ibm.github.io/opensource-ai-workshop/lab-5/), we'll move towards code-generation and learn how to use a local coding assistant.
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docs/lab-5/README.md

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## Conclusion
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With your AI coding assistant now set up, move on to [Lab 6](https://ibm.github.io/opensource-ai-workshop/lab-6/) and actually use it!
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docs/lab-6/README.md

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You can also take a look at the [Continue documentation](https://docs.continue.dev/chat/how-to-use-it), or at least have it open in case you want to refer to it.
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Now that our local AI co-pilot is up and running, let’s put it to work. The following examples will focus on `python`, but the same approach applies to other languages like `go`, `javascript`, or `rust`.
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Now that our local AI co-pilot is up and running, let’s put it to work. The following examples will focus on `python`, but the same approach applies to other languages like `go`, `javascript`, or `rust`.
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## Conclusion
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This lab was all about using our local, open-source AI co-pilot to write complex code in Python. By combining Continue and Granite-Code, we were able to generate code, explain functions, write tests, and add comments to our code!
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docs/lab-7/README.md

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It won't be exactly what we are looking for, but it's enough to now see that the Granite model can
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leverage the local RAG and in turn can _look things up_ for you. You'll need some prompt engineering
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to get exactly what you want but this is just the start of leveraging the AI!
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