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Eugene Yurtsev 2024-10-22 14:29:21 -04:00
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docs/docs/concepts/example_selectors.mdx
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## Overview
One common prompting technique for achieving better performance is to include examples as part of the prompt. This is known as [few-shot prompting](/docs/concepts/few-shot-prompting).
One common prompting technique for achieving better performance is to include examples as part of the prompt. This is known as [few-shot prompting](/docs/concepts/few_shot_prompting).
This gives the [language model](/docs/concepts/chat_models/) concrete examples of how it should behave.
Sometimes these examples are hardcoded into the prompt, but for more advanced situations it may be nice to dynamically select them.

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docs/docs/concepts/index.mdx
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- **[Output Parsers](/docs/concepts/output_parsers)**: Output parsers are responsible for taking the output of a model and transforming it into a more suitable format for downstream tasks. Output parsers were primarily useful prior to the general availability of [chat models](/docs/concepts/chat_models) that natively support [tool calling](/docs/concepts/tool_calling) and [structured outputs](/docs/concepts/structured_outputs).
- **[Few shot prompting](/docs/concepts/few_shot_prompting)**: Few-shot prompting is a technique used improve the performance of language models by providing them with a few examples of the task they are expected to perform.
- **[Example Selectors](/docs/concepts/example_selectors)**: Example selectors are used to select examples from a dataset based on a given input. They can be used to select examples randomly, by semantic similarity, or based on some other constraints. Example selectors are used in few-shot prompting to select examples for a prompt.
## Glossary
- **[AIMessageChunk](/docs/concepts/messages#aimessagechunk)**: A partial response from an AI message. Used when streaming responses from a chat model.