🤖 Tuesday Morning: AI & Machine Learning English

Good morning! Today we’re diving into the language of Artificial Intelligence and Machine Learning — the vocabulary every tech professional needs when discussing LLMs, agents, prompting strategies, RAG pipelines, and model inference. These words are essential for reading papers, attending conferences, writing docs, and collaborating with AI teams worldwide.

📖 Word of the Day

Inference

noun — /ˈɪn.fər.əns/

Vietnamese meaning: Suy luận / Quá trình suy diễn — trong AI, đây là bước mô hình đã được huấn luyện tạo ra kết quả dự đoán từ dữ liệu đầu vào mới.

Part	Sound	Tip
In-	/ɪn/	Short “i”, like “in the box”
-fer-	/fər/	Unstressed, quick schwa — “fur”
-ence	/əns/	Also unstressed, like “ents”

🔊 Full pronunciation: IN-fər-əns (stress on the FIRST syllable)

3 Example Sentences:

“The inference latency of this model is under 200 milliseconds, which is fast enough for real-time applications.”
“We moved inference to the edge to reduce round-trip time and improve user experience.”
“During inference, the model doesn’t update its weights — it simply predicts based on what it learned during training.”

🔗 Useful Links:

📚 Vocabulary Table

Five essential AI/ML phrases you’ll encounter in papers, team meetings, and documentation:

Phrase	Vietnamese	Example Sentence
Retrieval-Augmented Generation (RAG)	Tăng cường truy xuất tài liệu khi sinh văn bản	”We built a RAG pipeline so the chatbot can answer questions based on our internal docs.”
Fine-tuning	Tinh chỉnh mô hình	”Fine-tuning the base model on our domain data improved accuracy by 18%.”
Hallucination	Ảo giác (mô hình bịa thông tin)	“The model was hallucinating — it confidently cited papers that don’t exist.”
Context window	Cửa sổ ngữ cảnh (giới hạn token đầu vào)	“GPT-4 has a 128k token context window, so it can process entire codebases at once.”
Prompt engineering	Kỹ thuật viết câu lệnh cho AI	”Good prompt engineering can dramatically improve the quality of model outputs without changing any weights.”

🗣️ Pronunciation Guide

Deep Dive: “Inference”

Let’s break it down syllable by syllable:

IN  -  fer  -  ence
/ɪn/ - /fər/ - /əns/
 ↑       ↓       ↓
STRESS  weak   weak

Common mistakes Vietnamese speakers make:

❌ in-FER-ence (stressing the middle syllable — wrong!)
❌ in-fer-ENCE (stressing the last syllable — wrong!)
✅ IN-fer-ence (stress the FIRST syllable — correct!)

The middle and last syllables are reduced — they’re quick and quiet. This is very common in English 3-syllable words ending in -ence (e.g., inference, reference, conference, difference).

🎯 Full Practice Sentence

Read this sentence aloud 3 times, clearly and at a natural pace:

“Running inference on a large language model requires significant GPU memory, especially when the context window is fully utilized.”

Focus on:

Stress: IN-fer-ence, LAN-guage, MO-del, SI-gni-fi-cant
Linking sounds: “large_language” → sounds like “larj-lang-gwij”
The -ture in “future” sounds like /tʃər/ — “chur”

✏️ Exercise 1 — Fill in the Blank

Choose the correct word to complete each sentence: (hallucination / fine-tuning / context window / RAG / inference)

The system performs __________ at the edge so users get responses in under 100ms.
We use a __________ pipeline to ground the model’s answers in our company’s knowledge base.
The model had a __________ — it invented a CEO name that doesn’t exist.
After __________ on our support tickets, the model became much better at routing issues.
The __________ of this model is 32,000 tokens, which limits how much history we can pass in.

✅ Show Answers

inference — The system performs inference at the edge so users get responses in under 100ms.
RAG — We use a RAG pipeline to ground the model’s answers in our company’s knowledge base.
hallucination — The model had a hallucination — it invented a CEO name that doesn’t exist.
fine-tuning — After fine-tuning on our support tickets, the model became much better at routing issues.
context window — The context window of this model is 32,000 tokens, which limits how much history we can pass in.

✏️ Exercise 2 — Translate to English

Translate these Vietnamese sentences into natural English. Try before checking!

“Mô hình này bị ảo giác rất nhiều — nó cứ bịa ra những nguồn tài liệu không tồn tại.”
“Chúng tôi đang tinh chỉnh mô hình nền trên tập dữ liệu pháp lý của công ty.”
“Kỹ thuật viết câu lệnh tốt có thể cải thiện kết quả mà không cần thay đổi trọng số của mô hình.”

✅ Show Suggested Answers

“This model hallucinates a lot — it keeps making up sources that don’t exist.”

💡 Note: “hallucinate” is now used as a verb in the AI context!
“We’re fine-tuning the base model on the company’s legal dataset.”

💡 “base model” = mô hình nền; “legal dataset” = tập dữ liệu pháp lý
“Good prompt engineering can improve outputs without changing the model’s weights.”

💡 “outputs” is more natural than “results” in a technical context here

💡 Idiom of the Day

”Garbage in, garbage out”

🇻🇳 “Đầu vào rác, đầu ra cũng rác” — nếu bạn cung cấp dữ liệu hoặc đầu vào kém chất lượng, kết quả cũng sẽ kém.

This classic computing phrase is extremely common in AI/ML discussions, especially when talking about training data quality or prompt design.

2 Example Sentences:

“Our model’s predictions are terrible because the training data is full of duplicates — classic garbage in, garbage out.”
“If you write vague, lazy prompts, you’ll get vague answers back. Garbage in, garbage out — it applies to prompt engineering too.”

🎥 Recommended Watching

Improve your AI vocabulary AND listening comprehension with these channels:

Channel / Video	Why Watch It	Level
3Blue1Brown — Neural Networks series	Visual explanations with clear, precise English narration. Great for learning technical terms in context.	Intermediate
Andrej Karpathy — Let’s build GPT from scratch	A world-class AI researcher explaining complex concepts clearly. Real-world AI vocabulary in natural speech.	Upper-Intermediate
AI Explained (YouTube Channel)	Short, focused videos on the latest AI research with accessible English. Perfect for 10-minute daily listening.	Intermediate

Listening tip: Watch once for understanding, then watch again reading the subtitles to connect sounds to words. Pause and repeat sentences that contain words from today’s lesson.

🎯 Daily Challenge

Today’s tiny action (takes ~5 minutes):

Find ONE article or tweet about AI/ML today (from X/Twitter, LinkedIn, or Hacker News). Read it and highlight 3 technical words you don’t know. Look them up, write the Vietnamese meaning, and use each one in a sentence of your own.

Post your 3 sentences in your English journal or share them with a colleague.

This habit — reading real English in your field every day — is the fastest path to professional fluency. 🚀

📊 Today’s Summary

Item	Content
🔤 Word of the Day	Inference /ˈɪn.fər.əns/ — suy luận / quá trình dự đoán của mô hình AI
📚 Key Phrases	RAG, fine-tuning, hallucination, context window, prompt engineering
💡 Idiom	Garbage in, garbage out
🎯 Challenge	Find an AI article, extract 3 new words, write example sentences

Come back this afternoon for the Noon Session — we’ll practice using these words in professional conversations and code review comments. Keep it up! 💪

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English Lesson — Tuesday Morning: AI & Machine Learning