𝗩𝗼𝗶𝗰𝗲 𝗔𝗜: 𝗡𝗟𝗚 - 𝗧𝘂𝗿𝗻𝗶𝗻𝗴 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝗜𝗻𝘁𝗼 𝗪𝗼𝗿𝗱𝘀

Voice AI listens (ASR), understands (NLU), and decides (Dialog Management).

But decisions aren't responses.
The system knows:
▶️ Action: inform
▶️ Flight: booked
▶️ Destination: Paris
▶️ Date: Dec 20
▶️ Confirmation: AB123

That's not what we say to a user.

This is where 𝗡𝗟𝗚 (Natural Language Generation) comes in.

It transforms structured data into natural speech:
Example:
🤖 "Great news! Your flight to Paris on December 20th is confirmed. Your confirmation number is AB123. Have a wonderful trip!"

𝗧𝗵𝗲 𝗡𝗟𝗚 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲:
1️⃣ 𝗖𝗼𝗻𝘁𝗲𝗻𝘁 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴
🔹"What information to convey?"
🔹Select facts, order them, prioritize.
2️⃣ 𝗦𝗲𝗻𝘁𝗲𝗻𝗰𝗲 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴
🔹"How to structure it?"
🔹One sentence or multiple?
🔹Combine facts?
3️⃣ 𝗦𝘂𝗿𝗳𝗮𝗰𝗲 𝗥𝗲𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻
🔹"What exact words to use?" .
🔹Grammar, vocabulary, tone, fluency.

𝗧𝗵𝗲 𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻:
🔹Templates → slot-filling.
🔹Statistical → n-grams, HMMs.
🔹Neural → Seq2Seq, Transformers.
🔹LLMs → GPT, Claude (SOTA) .
Below are 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻s based on use case:
🔹Need predictability → Templates.
🔹Need natural variety → LLM.
🔹Need both → Hybrid (LLM + guardrails).

The difference between a robotic assistant and a delightful one? NLG.