Technical practice
How would you design a Python-based RAG pipeline for an enterprise Q&A assistant?
What to coverSuggested answer focus: Walk through document ingestion, indexing, retrieval, prompt construction and response generation. Explain how you would check whether retrieved context supports an answer and where you would look first if answer quality declined.
How would you improve a prompt when an LLM gives answers that go beyond the retrieved context?
What to coverSuggested answer focus: Discuss instructions for using context, handling missing evidence and testing prompts against representative questions. Distinguish a prompt problem from a retrieval problem before changing either component.
How would you choose chunking and embedding strategies for a knowledge-driven application?
What to coverSuggested answer focus: Compare chunk boundaries, overlap and embedding choices in terms of retrieval relevance and context preservation. Describe how you would test those choices using questions whose answers span different parts of a document.
What would guide your choice of a vector database for a RAG application?
What to coverSuggested answer focus: Use the posting's examples—Pinecone, Weaviate, FAISS and Milvus—to discuss relevant differences without claiming one is required. Consider retrieval features, operational needs and how you would measure search quality for the application.
How would you troubleshoot poor answers traced to a PDF or document extraction pipeline?
What to coverSuggested answer focus: Check whether extraction preserved text, reading order and useful document structure before examining chunking or retrieval. Explain how you would trace a failed question back to the extracted source and its indexed content.
When would you try query rewriting, multi-vector retrieval or reranking?
What to coverSuggested answer focus: Explain the retrieval failure each approach might address and how the approaches could interact. Compare potential relevance gains with added complexity and latency, using an evaluation set to decide whether a change helps.
How would you evaluate and diagnose a RAG pipeline that retrieves relevant passages but still produces weak answers?
What to coverSuggested answer focus: Separate retrieval quality from answer quality by inspecting retrieved passages, prompts and generated responses. Suggest measures tied to the task, such as whether answers address the question and remain supported by the retrieved content.
How would you design a conversational Q&A assistant with a simple web UI?
What to coverSuggested answer focus: Cover how conversational context reaches the retrieval and generation steps, and how the interface presents answers or uncertainty. The posting calls for basic HTML, CSS or React integration, so keep the UI discussion at that level rather than assuming a particular frontend stack.
How would you approach natural-language questions spanning structured data and documents?
What to coverSuggested answer focus: Contrast Text-to-SQL with retrieval from unstructured content, and explain how you would decide which source a question needs. As a preparation suggestion, discuss checking a generated query and its result before using them in an answer; the posting does not prescribe an implementation.
When would an orchestration or agentic framework help this application?
What to coverSuggested answer focus: Describe how a framework such as LangChain, LangGraph or LlamaIndex might organize retrieval and generation steps. Compare that with a simpler Python implementation, including the debugging and complexity trade-offs; familiarity, not use of a specific framework, is stated.
Behavioural practice
Tell me about a time you improved a knowledge-driven application's answer quality.
What to coverSuggested answer focus: Give the situation, the quality problem you observed, your actions and the result. Make clear what evidence showed improvement and which part of the pipeline you changed.
Describe a time you had to choose between competing technical approaches under practical constraints.
What to coverSuggested answer focus: Use a real situation, your decision process, the action you took and the result. A retrieval or application-design trade-off would be relevant preparation, but the posting does not specify a particular project or constraint.
Tell me about a time you worked with others to turn a user question or need into a working application.
What to coverSuggested answer focus: Set out the situation, how you clarified the need and coordinated your contribution, and the result. An example involving a chatbot or Q&A application would connect closely to the stated technical focus.
Describe a time feedback changed your approach to a technical solution.
What to coverSuggested answer focus: Explain the situation, the feedback, what you changed and the outcome. As a preparation suggestion, choose an example that shows how you tested the change and worked constructively with others.