LangChainGPT-LangChain-based AI model integration

Example

Managing and optimizing prompts to improve the quality of interactions with the LLM.

Scenario

A developer creating a chatbot can use prompt management to ensure that the prompts provided to the LLM are structured in a way that generates the most accurate and relevant responses. For instance, prompts can be dynamically constructed based on user input to provide contextually appropriate answers.

Example

Combining multiple LLM calls or integrating LLMs with other utilities to create multi-step workflows.

Scenario

In a customer support application, a chain might be used to first extract the user's query, then consult a database for relevant information, and finally generate a response using the LLM. This multi-step process ensures that the response is both accurate and informative.

Example

Creating agents that can make decisions about which actions to take based on the input they receive.

Scenario

An agent could be developed to handle e-commerce customer inquiries. It might first check inventory levels, then determine the best shipping options, and finally generate a message to the customer, all without human intervention.