Word of the Day: fine-tuning
IPA: /ˈfaɪn ˌtjuːnɪŋ/ Vietnamese: tinh chỉnh mô hình Part of speech: verb (to fine-tune) / noun (fine-tuning)
Reference: Merriam-Webster — fine-tune
Example Sentences
- “We spent two weeks fine-tuning the model on our internal customer support dataset before deploying it to production.”
- “The fine-tuning process reduced hallucinations by nearly 40% compared to the base model.”
- “Instead of fine-tuning a large LLM, the team decided to use prompt engineering first to see if that was enough.”
Vocabulary Table
| Term | IPA | Vietnamese | Example |
|---|---|---|---|
| fine-tuning | /ˈfaɪn ˌtjuːnɪŋ/ | tinh chỉnh mô hình | ”Fine-tuning on domain-specific data improved accuracy significantly.” |
| pre-training | /ˌpriːˈtreɪnɪŋ/ | tiền huấn luyện | ”The model’s pre-training cost millions of dollars in compute.” |
| inference | /ˈɪnfərəns/ | suy luận / chạy mô hình | ”Inference latency dropped to under 200ms after we switched to a quantized model.” |
| embedding | /ɪmˈbɛdɪŋ/ | nhúng / vectơ nhúng | ”We store document embeddings in a vector database for fast semantic search.” |
| prompt engineering | /prɒmpt ˌɛndʒɪˈnɪərɪŋ/ | kỹ thuật thiết kế câu lệnh | ”Good prompt engineering can get surprisingly far before you need fine-tuning.” |
Pronunciation Guide
Practice Sentence
“After pre-training, fine-tuning with task-specific data makes inference much more reliable.”
Breakdown
| Word | Stress | Sound tip |
|---|---|---|
| pre-training | PRE-train-ing | Stress on the first syllable: PREE-tray-ning |
| fine-tuning | FINE-tune-ing | Both words are stressed; the hyphen links them as one concept: FYNE-tyoo-ning |
| inference | IN-fer-ence | Three syllables; many people mistakenly say four. It is IN-frəns, not in-FEER-ence |
Tip: Say it slowly three times, then at normal speed:
- “pre-TRAIN-ing… FINE-tune-ing… IN-frəns…”
- “pre-training, fine-tuning, inference…”
- “After pre-training, fine-tuning with task-specific data makes inference much more reliable.”
Exercises
Exercise 1: Fill in the Blank
Choose the correct word: fine-tuning / pre-training / inference / embedding / prompt engineering
- The model was built on a massive corpus during __________, before we ever touched it.
- We reduced our cloud bill by 60% by optimizing the __________ pipeline with a smaller quantized model.
- The team tried __________ first — crafting better system prompts — before committing to a full retraining cycle.
- Each document is converted into an __________ and stored in Pinecone for semantic retrieval.
- Three months of __________ on our legal documents gave the chatbot the domain knowledge it needed.
See Answers
- pre-training
- inference
- prompt engineering
- embedding
- fine-tuning
Exercise 2: Translation Challenge
Translate these Vietnamese sentences into natural English. Use the vocabulary from today’s lesson where possible.
- “Chúng tôi đang tinh chỉnh mô hình trên dữ liệu y tế để nó hiểu được thuật ngữ chuyên ngành.”
- “Chi phí suy luận quá cao, nên chúng tôi cần tối ưu hóa trước khi ra mắt sản phẩm.”
- “Kỹ thuật thiết kế câu lệnh tốt có thể tiết kiệm cho bạn hàng tuần tinh chỉnh mô hình.”
See Answers
- “We are fine-tuning the model on medical data so it understands domain-specific terminology.”
- “The inference cost is too high, so we need to optimize before the product launch.”
- “Good prompt engineering can save you weeks of fine-tuning.”
Notes:
- Sentence 1: “domain-specific terminology” is the natural technical phrase — avoid “specialized vocabulary of the field.”
- Sentence 2: “inference cost” is the standard industry term, not “reasoning cost.”
- Sentence 3: Note the contrast structure — “can save you X of Y” is a common English pattern for showing trade-offs.
Idiom of the Day: “in the weeds”
Meaning: Lost or stuck in too many small details; unable to see the bigger picture.
Vietnamese equivalent: “sa vào chi tiết vụn vặt” / “bị ngập trong chi tiết nhỏ”
Where you’ll hear it: Code reviews, sprint retros, AI experiment post-mortems, design meetings.
Examples
- “We spent the whole meeting in the weeds about hyperparameter choices and never agreed on a deployment date.”
- “Let’s not get in the weeds on the tokenizer settings — the bigger issue is dataset quality.”
- “I was so in the weeds with the fine-tuning config that I missed the obvious data leakage problem.”
Usage tip: It is often used to redirect a conversation — “I think we’re getting in the weeds here. Can we zoom out?” — so it signals both a problem and a proposed fix.
Mini Dialogue: Two Engineers Discussing Fine-Tuning
Minh: Hey, how is the LLM fine-tuning going? Are we still on track for the demo?
Sara: Honestly, we got a bit in the weeds with the learning rate scheduling. But the model is looking good now — inference latency is under 300ms.
Minh: Nice. Did you end up using the full customer dataset or just a subset?
Sara: A subset — about 8,000 examples. Pre-training already gave it strong general knowledge, so fine-tuning on a clean, focused set was enough.
Minh: Smart. Did prompt engineering help at all, or did you go straight to fine-tuning?
Sara: We tried prompts first, but the outputs were inconsistent. Fine-tuning was the right call — the tone and format are much more reliable now.
Challenge: 2-Minute Action
Open your most recent work message, email, or Slack thread about any AI or tech topic.
Rewrite it (or write a reply) using at least two of today’s terms: fine-tuning and inference.
Example: If your message says “the AI model is slow and sometimes wrong,” try rewriting it as:
“Inference latency is higher than expected, and the outputs are inconsistent. We may need to look at fine-tuning on a cleaner dataset to improve reliability.”
This is the kind of precise, professional language that sets strong technical communicators apart. Two minutes — go.