grounding
Applied AI interview questions tagged grounding, across every topic.
6 questions · 1 unlocked for you
Concepts behind "grounding"
The curriculum that explains the ideas these questions test.
Foundational
HallucinationA hallucination is fluent, confident output that is wrong or unsupported. It happens because a language model is trained to produce plausible continuations, not to know what it knows; it has no built-in truth check. You reduce it with grounding (RAG), letting the model abstain, low temperature on factual tasks, and verification, and you detect it with faithfulness checks against sources. Applied-AI interviews probe it because hallucination is the number-one reason LLM features fail in production, and because the fix is system design, not a magic prompt.🧠 Foundations of LLMs & GenAI
Foundational
The RAG PipelineRetrieval-Augmented Generation grounds an LLM in external knowledge: at query time you retrieve the most relevant chunks from a knowledge base and put them in the prompt, so the model answers from real sources instead of memory. It is the default fix for hallucination and stale knowledge, and it updates without retraining. The pipeline is ingest and chunk, embed and index, retrieve (often rerank), then generate with citations. Applied-AI interviews probe it because RAG is the modal production LLM architecture.🤖 Retrieval & Agents
Foundational
Citations and GroundingGrounding means the model answers only from provided sources; citations make each claim traceable to the exact passage that supports it. Together they are the trust mechanism of RAG: they let users verify, let you detect hallucination (an uncited or unsupported claim is a red flag), and are mandatory in high-stakes domains. Applied-AI interviews probe it because 'it gave a great answer' is worthless if you cannot tell whether it is true, and citations are how production AI earns trust.🤖 Retrieval & Agents