Generative AI skills—cut through the noise

What I'm seeing in the field and recent interviews

I’ve spoken to some recent candidates who worked with NLP recently. I’m going to summarize the skills that the successful ones had.

• Some of them worked with NLP for a longer time, but many of them were working with NLP for just the last year with the Generative AI boom. Regardless, they were able to explain models such as BERT well. BERT has been around since 2018 and started to power search engines (Bing, Google) in 2019 and we’ve all likely interacted with it. It’s still very relevant; even many newer “hyped” models are still built on top of it. Example: in vector embeddings, E5 and SPLADE, ELSER all use BERT somewhere in their architecture. (Basically, the foundations)

• They had tried different models for their use case, and can explain why. For example, embeddings as part of their LLM-powered project, evaluating a few models on the MTEB leaderboard, not just taking the “top” one and using it.

• LangChain has been sticking around through the hype; I think it’s worth learning and doing a small project yourself. [At Elastic we use it (and LangSmith) ourselves to create the Elastic AI Assistant, a GPT4 (and other models) powered chat that uses many new Generative AI tools in the background, such as RAG and Elasticsearch as the vector database.]

Since I’ve been recently interviewing candidates for our company, I hope this insight was helpful to see the perspective on the other side of the table.

Other news: course, book for ML interviews

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