Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãOPromptsPrompt templatesConnecting to a Feature StoreCustom prompt templateFew-shot prompt templatesFew-shot examples for chat modelsFormat template outputTemplate formatsTypes of MessagePromptTemplatePartial prompt templatesCompositionSerializationPrompt pipeliningValidate templateExample selectorsLanguage modelsOutput parsersRetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesModel I/‚ÄãOPromptsPrompt templatesFew-shot examples for chat modelsOn this pageFew-shot examples for chat modelsThis notebook covers how to use few-shot examples in chat models. There does not appear to be solid consensus on how best to do few-shot prompting, and the optimal prompt compilation will likely vary by model. Because of this, we provide few-shot prompt templates like the FewShotChatMessagePromptTemplate as a flexible starting point, and you can modify or replace them as you see fit.The goal of few-shot prompt templates are to dynamically select examples based on an input, and then format the examples in a final prompt to provide for the model.Note: The following code examples are for chat models. For similar few-shot prompt examples for completion models (LLMs), see the few-shot prompt templates guide.Fixed Examples‚ÄãThe most basic (and common) few-shot prompting technique is to use a fixed prompt example. This way you can select a chain, evaluate it, and avoid worrying about additional moving parts in production.The basic components of the template are:examples: A list of dictionary examples to include in the final prompt.example_prompt: converts each example into 1 or more messages through its format_messages method. A common example would be to convert each example into one human
This notebook covers how to use few-shot examples in chat models. There does not appear to be solid consensus on how best to do few-shot prompting, and the optimal prompt compilation will likely vary by model. Because of this, we provide few-shot prompt templates like the FewShotChatMessagePromptTemplate as a flexible starting point, and you can modify or replace them as you see fit.
This notebook covers how to use few-shot examples in chat models. There does not appear to be solid consensus on how best to do few-shot prompting, and the optimal prompt compilation will likely vary by model. Because of this, we provide few-shot prompt templates like the FewShotChatMessagePromptTemplate as a flexible starting point, and you can modify or replace them as you see fit. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãOPromptsPrompt templatesConnecting to a Feature StoreCustom prompt templateFew-shot prompt templatesFew-shot examples for chat modelsFormat template outputTemplate formatsTypes of MessagePromptTemplatePartial prompt templatesCompositionSerializationPrompt pipeliningValidate templateExample selectorsLanguage modelsOutput parsersRetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesModel I/‚ÄãOPromptsPrompt templatesFew-shot examples for chat modelsOn this pageFew-shot examples for chat modelsThis notebook covers how to use few-shot examples in chat models. There does not appear to be solid consensus on how best to do few-shot prompting, and the optimal prompt compilation will likely vary by model. Because of this, we provide few-shot prompt templates like the FewShotChatMessagePromptTemplate as a flexible starting point, and you can modify or replace them as you see fit.The goal of few-shot prompt templates are to dynamically select examples based on an input, and then format the examples in a final prompt to provide for the model.Note: The following code examples are for chat models. For similar few-shot prompt examples for completion models (LLMs), see the few-shot prompt templates guide.Fixed Examples‚ÄãThe most basic (and common) few-shot prompting technique is to use a fixed prompt example. This way you can select a chain, evaluate it, and avoid worrying about additional moving parts in production.The basic components of the template are:examples: A list of dictionary examples to include in the final prompt.example_prompt: converts each example into 1 or more messages through its format_messages method. A common example would be to convert each example into one human