Artificial Intelligence Interview Questions

Shalin G. - "Tell me about the most recent product you launched that you are very proud of?"See full answer

Debajyoti B. - "As per my understanding, Success of the AI product/feature has to be measured in 2 aspects: success of the problem it solves success of the model used Success of the problem- start with the overall business goal of the problem(acquisition, retention etc) & the high level metric(essentially north star) then comes the product/feature level goals & metrics(conversion, engagement, clicks etc) Success of the model used- general: precision, recall, latency ethical: bias, safety busine"See full answer

c8lin - "Clarify Is this geared towards a specific side of the marketplace? Goal Ubers mission is to provide lines of business that simplify life for users and get them/things they need from point a to point b Uber rides: point a to point b AI product: reduce user effort in existing experience and proactively highlight users needs Interviewer interjection: Why do we as Uber have the right to win this area or what about the strength of uber means we should be the winner? Answer provided: Given Ub"See full answer

S R. - "For RAG systems, You need to evaluate the retrieval and generation. Typically, you have golden truth question and answers as evaluation data set. For retrieval, check if the retrieved contexts are relevant for the question For generation evaluation, You check the semantic similarity between the golden truth and RAG generated answer. Apart from this, you can evaluate the output using frameworks like RAGAS where the answers generated are evaluated based on completeness, faithfulness, to"See full answer

Mayank M. - "There are 2 main methods Intrinsic Evaluation i) Preplexity ii) BLEU Extrinsic Evaluation i) Response consistency/ Correctness / Factual score/ Security However, this question requires a follow-up question and clarification about where we are going to use the LLM models."See full answer

Sumit P. - "CQs: Content moderation system finds Inappropriate contents - profanity, violence, privacy concerning. Misinformation - false info, false claim, fomenting wrong views PII Misinformation → Wrong info Twisted info Incomplete info Goal - reliability and trust on the platform Long term increased engagement on informational content RAG system - what and why? RAG system has 3 components Brain - reasoning models Tool"See full answer

Alex N. - "The adjusting context window size in LLM change trade off between reasoning capability, accuracy, computation cost. It influence how attention mechanist allocate resources across the input. Longer context window let it you input greater number of words and have more context to generate proper next token. However llms have lost in the middle issue. They remember the beginning of text and end of text but lost information located in the middle of long input. Another problem is Attention Dilution."See full answer

Anonymous Partridge - "We will have to use a second more powerful LLM Model to validate the answers. LLM as a judge"See full answer