Databricks Generative AI Engineer Associate Question 100
Select 2Your team is developing a Generative AI application that requires chaining multiple LLM (Large Language Model) calls, integrating with external APIs for real-time data, and enabling memory capabilities for personalized responses. Which tool(s) would be most appropriate to use in this scenario?
- A
LangChain
- B
PyTorch
- C
Hugging Face Transformers
- D
GPT-3 Playground
- E
LlamaIndex
Show answer and explanation
Correct answers: A, E
Explanation
LangChain and LlamaIndex are tools specifically tailored for building sophisticated Generative AI applications. LangChain enables chaining LLM calls, API integrations, and memory capabilities, which are crucial for personalized and dynamic Generative AI applications. LlamaIndex complements this by facilitating the integration of external data into the application. Other tools like PyTorch and Hugging Face Transformers serve different purposes and are not designed for this specific scenario.
- A. Correct.
LangChain is designed for creating applications that require chaining multiple LLM calls, integrating external data sources, and adding memory capabilities. It is highly suitable for the described use case.
- B. Incorrect.
PyTorch is a deep learning framework primarily used for model training and deployment, not for chaining LLM calls or integrating APIs in a Generative AI application.
- C. Incorrect.
Hugging Face Transformers is a library that provides pre-trained models and tools for fine-tuning. While useful for model-related tasks, it does not specialize in chaining LLM calls or integrating APIs.
- D. Incorrect.
GPT-3 Playground is mainly an interface for experimenting with OpenAI's GPT-3 model. It is not a tool for building complex applications or integrating external data.
- E. Correct.
LlamaIndex (formerly known as GPT Index) excels at connecting LLMs with external data sources, making it suitable for the described scenario alongside LangChain.