forked from Archives/langchain
50 lines
1.4 KiB
ReStructuredText
50 lines
1.4 KiB
ReStructuredText
Prompts
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==========================
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.. note::
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`Conceptual Guide <https://docs.langchain.com/docs/components/prompts>`_
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The new way of programming models is through prompts.
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A "prompt" refers to the input to the model.
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This input is rarely hard coded, but rather is often constructed from multiple components.
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A PromptTemplate is responsible for the construction of this input.
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LangChain provides several classes and functions to make constructing and working with prompts easy.
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This section of documentation is split into four sections:
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**LLM Prompt Templates**
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How to use PromptTemplates to prompt Language Models.
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**Chat Prompt Templates**
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How to use PromptTemplates to prompt Chat Models.
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**Example Selectors**
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Often times it is useful to include examples in prompts.
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These examples can be hardcoded, but it is often more powerful if they are dynamically selected.
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This section goes over example selection.
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**Output Parsers**
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Language models (and Chat Models) output text.
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But many times you may want to get more structured information than just text back.
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This is where output parsers come in.
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Output Parsers are responsible for (1) instructing the model how output should be formatted,
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(2) parsing output into the desired formatting (including retrying if necessary).
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Go Deeper
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---------
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.. toctree::
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:maxdepth: 1
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./prompts/prompt_templates.rst
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./prompts/chat_prompt_template.ipynb
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./prompts/example_selectors.rst
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./prompts/output_parsers.rst
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