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.