AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
What is 'natural language generation' (NLG) and how does it differ from NLU?
⚠ Common exam trap
It's easy for candidates to confuse NLG with hardware acceleration or assume NLG and NLU are interchangeable, when the exam specifically tests the clear distinction between understanding input (NLU) and generating output (NLG) as separate AI workloads.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
NLU is understanding language input; NLG is producing natural language output from data
Natural Language Generation (NLG) is the AI capability that produces coherent, human-readable text or speech from structured data or other inputs. It differs from Natural Language Understanding (NLU), which focuses on interpreting and extracting meaning from language input. Option B correctly identifies NLU as understanding input and NLG as generating output, which is the fundamental distinction between these two subfields of natural language processing (NLP).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
NLG is the same as NLU — both involve processing natural language
Why it's wrong here
NLU and NLG are not the same; they are complementary but distinct tasks in natural language processing. NLU, or Natural Language Understanding, parses incoming text to derive meaning, intent, and entities, whereas NLG, or Natural Language Generation, produces new human-readable text from structured data or prompts. Though both process natural language, one consumes language to extract information while the other produces language from information.
- ✓
NLU is understanding language input; NLG is producing natural language output from data
Why this is correct
NLU is the process of understanding language input—interpreting user queries, recognizing intents, and extracting entities—while NLG is the process of producing natural language output from data or structured input. Large language models (LLMs) perform both simultaneously: they encode the input's meaning (NLU) and then autoregressively generate a coherent response (NLG). This dual ability powers conversational AI, but the two functions remain conceptually separate.
- ✗
NLG is a hardware component that accelerates language model inference
Why it's wrong here
NLG is not hardware; it is an AI software capability for text generation. Inference acceleration for language models is provided by specialized hardware like GPUs and TPUs, which contain tensor cores and parallel processing units optimized for matrix operations. NLG represents the model's learned function that predicts the next token in a sequence, and while it runs on such hardware, it is not itself a physical component.
- ✗
NLU works on text; NLG works only on spoken audio
Why it's wrong here
This option incorrectly restricts NLG to spoken audio. Both NLU and NLG operate primarily on text: NLU analyzes textual input, and NLG outputs written language. Spoken output requires an additional text-to-speech (TTS) component that converts the generated text into audio, so NLG itself does not work on audio.
Go deeper
Related to this question
Learn chapter
Large Language Models (LLMs)
Key term
NLP
NLP (Natural Language Processing) is a branch of artificial intelligence that enables computers to understand, interpret, and generate human language in a way that is meaningful and useful.
Key term
Natural language processing
Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret, and respond to human language in a way that is both meaningful and useful.
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