GENERATIVE-AI-ENGINEER-ASSOCIATE · Question #17
What is the most suitable library for building a multi-step LLM-based workflow?
The correct answer is D. LangChain. LangChain (option D) is purpose-built for constructing multi-step LLM workflows through its 'chain' abstraction, which connects prompts, LLMs, retrievers, memory, tools, and agents into composable pipelines. Pandas (A) is a data manipulation library with no LLM workflow…
Question
What is the most suitable library for building a multi-step LLM-based workflow?
Options
- APandas
- BTensorFlow
- CPySpark
- DLangChain
How the community answered
(58 responses)- A3% (2)
- B7% (4)
- C2% (1)
- D88% (51)
Explanation
LangChain (option D) is purpose-built for constructing multi-step LLM workflows through its 'chain' abstraction, which connects prompts, LLMs, retrievers, memory, tools, and agents into composable pipelines. Pandas (A) is a data manipulation library with no LLM workflow capabilities. TensorFlow (B) is a deep learning framework focused on model training and inference, not orchestrating LLM calls. PySpark (C) is a distributed data processing engine. Only LangChain is specifically designed for the LLM application layer.
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