In the second part of our LangChain series, we'll explore PromptTemplates, FewShotPromptTemplates, and example selectors. These are key features in LangChain that support prompt engineering for LLMs like OpenAI's GPT 3, Cohere, and Hugging Face's OS alternatives.

LangChain is a popular framework that allows users to quickly build apps and pipelines around Large Language Models. It integrates directly with OpenAI's GPT-3 and GPT-3.5 models and Hugging Face's open-source alternatives like Google's flan-t5 models.

It can be used for chatbots, Generative Question-Answering (GQA), Retrieval Augmented Generation (RAG), summarization, and much more.

The core idea of the library is that we can "chain" together different components to create more advanced use cases around LLMs. Chains may consist of multiple components from several modules. We'll explore all of this in these videos.

Part 1 (Intro): [ Ссылка ]
Part 3 (Chains): [ Ссылка ]

📌 Code notebook:
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🌲 Pinecone article:
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00:00 Why prompts are important
02:42 Structure of prompts
04:10 Langchain code Setup
05:56 Langchain's PromptTemplates
08:34 Few shot learning with LLMs
13:04 Few shot prompt templates in Langchain
16:09 Length-based example selectors
21:19 Other Langchain example selectors
22:12 Final notes on prompts + Langchain

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