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Merge pull request #299 from trojan-bumble-bee/website/docs/minor-fixes
fix(docs): minor grammar fixes
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website/docs/chains/02-map-reduce-chains.md

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```
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n this example, we start by importing the necessary modules and defining the main function. We then create a new ChatGPT executor using the executor!() macro.
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In this example, we start by importing the necessary modules and defining the main function. We then create a new ChatGPT executor using the executor!() macro.
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Next, we create the "map" and "reduce" steps using Step::for_prompt_template(). The "map" step is responsible for summarizing each article chunk, while the "reduce" step combines the summaries into a single output.
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website/docs/getting-started-tutorial/01-setting-up-a-project.md

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:::tip
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Having problems? Don't worry reach out on [discord](https://discord.gg/kewN9Gtjt2) and we will help you out.
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Having problems? Don't worry, reach out on [discord](https://discord.gg/kewN9Gtjt2) and we will help you out.
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website/docs/getting-started-tutorial/02-generating-your-first-llm-output.md

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:::tip
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Having problems? Don't worry reach out on [discord](https://discord.gg/kewN9Gtjt2) and we will help you out.
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Having problems? Don't worry, reach out on [discord](https://discord.gg/kewN9Gtjt2) and we will help you out.
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website/docs/getting-started-tutorial/03-using-prompt-templates-and-parameters.md

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Having problems? Don't worry reach out on [discord](https://discord.gg/kewN9Gtjt2) and we will help you out.
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Having problems? Don't worry, reach out on [discord](https://discord.gg/kewN9Gtjt2) and we will help you out.
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website/docs/getting-started-tutorial/04-building-a-multi-step-chain.md

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Having problems? Don't worry reach out on [discord](https://discord.gg/kewN9Gtjt2) and we will help you out.
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Having problems? Don't worry, reach out on [discord](https://discord.gg/kewN9Gtjt2) and we will help you out.
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- The first step has a prompt to make a personalized birthday email for a company.
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- The second step has a prompt to summarize the email into a tweet.
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Both prompts use placeholders (e.g., `{{name}}`, `{{date}}`, and `{{text}}`) that will be replaced with specific values later. Importantly the value of `{{text}}` will replaced by result of the first step in the chain.
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Both prompts use placeholders (e.g., `{{name}}`, `{{date}}`, and `{{text}}`) that will be replaced with specific values later. Importantly, the value of `{{text}}` will be replaced by result of the first step in the chain.
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5. We run the `Chain` with the provided parameters:
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1. Use descriptive and clear instructions for the system role to help guide the LLM.
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2. Keep the chain as short and simple as possible. Longer chains are harder to manage and debug.
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3. Test each step independently before in
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3. Test each step independently before testing the entire sequence.
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For the next tutorial we will switch our focus from sequential to map-reduce chains. Map reduce chains are more complicated than sequential chains but allow us to do things that sequential chains can't. Stay tuned!

website/docs/getting-started-tutorial/05-summarizing-text-with-map-reduce.md

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Having problems? Don't worry reach out on [discord](https://discord.gg/kewN9Gtjt2) and we will help you out.
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Having problems? Don't worry, reach out on [discord](https://discord.gg/kewN9Gtjt2) and we will help you out.
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Map-reduce is a powerful technique for processing and aggregating data in parallel. In this tutorial, we'll explore how to use map-reduce in `llm-chain` to summarize text effectively. We'll cover implementing a basic map-reduce for text summarization.
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To start create a file named in "article_to_summarize.md" take the content of a wikipedia article and paste it in there.
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To start, create a file named "article_to_summarize.md", take the content of a wikipedia article and paste it in there.
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Here's a Rust program that demonstrates how to create a map-reduce chain for summarizing text:
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