grounding
AI, ML & GenAI interview questions tagged grounding, across every topic.
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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 arises 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. AI, ML, and GenAI engineer 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 anchors an LLM in outside knowledge: when a query arrives you pull the most relevant chunks from a knowledge base into the prompt, letting the model respond from actual sources rather than memory. This is the go-to remedy for hallucination and outdated knowledge, and refreshing it needs no retraining. Its stages are ingest and chunk, embed and index, retrieve (frequently rerank), then generate with citations. AI, ML, and GenAI interviews test it because RAG is the most common production LLM architecture.🤖 Retrieval & Agents
Foundational
Citations and GroundingGrounding means the model answers only from supplied sources; citations make each claim traceable to the exact passage backing it. Together they form RAG's trust mechanism: they let users verify, let you catch hallucination (an uncited or unsupported claim is a red flag), and are required in high-stakes domains. Applied-AI interviews cover it because 'it gave a great answer' means nothing if you cannot tell whether it is true, and citations are how production AI earns trust.🤖 Retrieval & Agents