Building LLM-Powered Applications

 

Getting Started with LangChain: A Beginner’s Guide to Building LLM-Powered Applications

Since the release of ChatGPT, large language models (LLMs) have gained a lot of popularity. Although you probably don’t have enough money and computational resources to train an LLM from scratch in your basement, you can still use pre-trained LLMs to build something cool, such as:

With their weird APIs and prompt engineering, LLMs are changing how we build AI-powered products. That’s why new developer tools are emerging everywhere under the term “LLMOps”.

LangChain is a framework built to help you build LLM-powered applications more easily by providing you with the following:

a generic interface to a variety of different foundation models (see Models),

a framework to help you manage your prompts (see Prompts), and

a central interface to long-term memory (see Memory), external data (see Indexes), other LLMs (see Chains), and other agents for tasks an LLM is not able to handle (e.g., calculations or search) (see Agents).

It is an open-source project (GitHub repository) created by Harrison Chase.

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