Prompting in LangChain

The recent emergence of Language Learning Models (LLMs) has introduced new tools, such as the LLM framework called LangChain. This versatile tool offers various features like different prompting methods, maintaining conversational context, and connecting to external tools. Prompting is a crucial task in building AI applications with LLMs, and this article extensively explores how to use LangChain for more complex prompts. The text covers: 1. Simple Prompts in LangChain: This section demonstrates the basic usage of LangChain prompting by creating a single prompt using the `PromptTemplate` object. It also explains how to add an LLM and create an `LLMChain`. 2. Multi Question Prompts: The article shows how to handle multiple questions within a single prompt using the same `PromptTemplate` object. 3. Few Shot Learning with LangChain Prompts: This section introduces "few shot learning," where users can teach AI how to behave by providing examples of desired responses. It demonstrates this feature using the `FewShotPromptTemplate`. 4. Token Limiting Your LangChain Prompts: To manage token usage and keep costs down, the article explains how to use the `LengthBasedExampleSelector` object to limit tokens in queries. 5. A Summary of Prompting in LangChain: The text concludes by summarizing the key points covered in the article about prompting with LangChain.