Have you worked with or considered LLM frameworks or generative AI in the context of data engineering?

Yes, I’ve explored several LLM frameworks such as LangChain, LlamaIndex, and Hugging Face’s Transformers for data engineering tasks. In practice, I use them for semantic search over large document collections, automated data labeling, and feature generation. For example, I built a pipeline that ingests raw logs, uses an LLM to extract structured entities, and stores them in a vector database for fast similarity queries. Challenges include managing inference latency, controlling costs on cloud GPUs, and ensuring data privacy by keeping sensitive data on-prem or using private endpoints. I also experiment with fine‑tuning smaller models on domain‑specific corpora to reduce token usage while maintaining accuracy.