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README.md

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## Prompting
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Buiding prompts can get messy. **Outlines** makes it easier to write and manage
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Building prompts can get messy. **Outlines** makes it easier to write and manage
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prompts by encapsulating templates inside "template functions".
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These functions make it possible to neatly separate the prompt logic from the
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import outlines
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examples = [
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("The food was digusting", "Negative"),
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("The food was disgusting", "Negative"),
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("We had a fantastic night", "Positive"),
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("Recommended", "Positive"),
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("The waiter was rude", "Negative")

docs/blog/posts/roadmap-2024.md

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# Roadmap for 2024
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Outlines is not even one year old and it's already gone a long way! As we just reached 4000k stars, and before laying out the roadmap for the following year, we would like to pause and thank all of you for supporting us, using and contributing to the library!
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Outlines is not even one year old and it's already gone a long way! As we just reached 4000 stars, and before laying out the roadmap for the following year, we would like to pause and thank all of you for supporting us, using and contributing to the library!
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![4000 stars](../assets/4000_stars.png)
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* How does Outlines work? What can you do with it?
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* What can you do with Outlines that is harder or impossible to do with other libraries?
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* How you can perform standard LLM workflows, for instance Chain of Thoughts, Tree of Throughts, etc?
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* How does Oultines integrates with the larger ecosysten, for instance other libraries like LangChain and LlamaIndex?
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* How you can perform standard LLM workflows, for instance Chain of Thoughts, Tree of Thoughts, etc?
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* How does Oultines integrates with the larger ecosystem, for instance other libraries like LangChain and LlamaIndex?
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### Simplify the integrations
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docs/quickstart.md

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### Prompt templates
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Prompting can lead to messy code. Outlines' prompt functions are python functions that contain a template for the prompt in their docstring. We use a powerful templating language to allow you to loop over lists, dictionnaries, add conditionals, etc. directly from the prompt. When called, a prompt function returns the rendered template:
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Prompting can lead to messy code. Outlines' prompt functions are python functions that contain a template for the prompt in their docstring. We use a powerful templating language to allow you to loop over lists, dictionaries, add conditionals, etc. directly from the prompt. When called, a prompt function returns the rendered template:
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```python
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import outlines

docs/reference/prompting.md

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!!! warning "Prompt rendering"
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Prompt functions are opinionated when it comes to prompt rendering. These opinions are meant to avoid common prompting errors, but can have unintended consequences if you are doing something unusuable. We advise to always print the prompt before using it. You can also [read the
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Prompt functions are opinionated when it comes to prompt rendering. These opinions are meant to avoid common prompting errors, but can have unintended consequences if you are doing something unusual. We advise to always print the prompt before using it. You can also [read the
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reference](#formatting-conventions) section if you want to know more.
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## Your first prompt

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