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β€Ždocs/docs_skeleton/docs/guides/safety/amazon_comprehend_chain.ipynbβ€Ž

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β€Ždocs/docs_skeleton/docs/guides/safety/index.mdxβ€Ž

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One of the key concerns with using LLMs is that they may generate harmful or unethical text. This is an area of active research in the field. Here we present some built-in chains inspired by this research, which are intended to make the outputs of LLMs safer.
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- [Moderation chain](/docs/use_cases/safety/moderation): Explicitly check if any output text is harmful and flag it.
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- [Constitutional chain](/docs/use_cases/safety/constitutional_chain): Prompt the model with a set of principles which should guide it's behavior.
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- [Moderation chain](/docs/guides/safety/moderation): Explicitly check if any output text is harmful and flag it.
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- [Constitutional chain](/docs/guides/safety/constitutional_chain): Prompt the model with a set of principles which should guide it's behavior.
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β€Ždocs/extras/guides/debugging.mdβ€Ž

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## Tracing
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Platforms with tracing capabilities like [LangSmith](/docs/guides/langsmith/) and [WandB](/docs/ecosystem/integrations/agent_with_wandb_tracing) are the most comprehensive solutions for debugging. These platforms make it easy to not only log and visualize LLM apps, but also to actively debug, test and refine them.
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Platforms with tracing capabilities like [LangSmith](/docs/guides/langsmith/) and [WandB](/docs/integrations/providers/wandb_tracing) are the most comprehensive solutions for debugging. These platforms make it easy to not only log and visualize LLM apps, but also to actively debug, test and refine them.
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For anyone building production-grade LLM applications, we highly recommend using a platform like this.
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β€Ždocs/extras/integrations/chat_loaders/facebook.ipynbβ€Ž

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"pygments_lexer": "ipython3",
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"version": "3.10.1"
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