How to Create Targeted Vocabulary Lists for Tech Professionals Using AI

Combine the seed word banks from the byoungd/English-level-up-tips repository with LLM enrichment to generate role-specific tech vocabularies that prioritize high-frequency terms over academic rarely-used words.

Tech professionals need high-frequency, reusable terms that appear in code, design discussions, documentation, and interview conversations. The byoungd/English-level-up-tips repository provides language-specific word banks (e.g., docs/threads/word-list/JavaScript.md, docs/threads/word-list/Go.md) and a comprehensive AI usage guide (docs/threads/part-1/7-ai.md) that together enable a scalable system to create targeted vocabulary lists using modern LLMs like OpenAI GPT-4o, Gemini, or Claude.

Why Targeted Vocabulary Outperforms Generic Lists

Learning generic academic words wastes time for software engineers. A list reflecting specific technology stacks—such as JavaScript, Rust, or Cloud Infrastructure—speeds up comprehension and expression in real-world engineering contexts. The repository already curates these foundational lists, allowing AI to handle the enrichment, frequency ranking, and example generation rather than manual curation.

The AI-Powered Workflow Architecture

The process follows a 六步闭环 (six-step closed loop) that mirrors the "词汇吸收闭环" (vocabulary absorption loop) described in the AI chapter, but automates the heavy lifting.

1. Define the Target Profile

Specify the job title, primary programming language, and domain. For example: "Senior Frontend Engineer, JavaScript/React, SaaS applications." This context determines which seed files to pull from the repository.

2. Extract Seed Terms from Repository Word Banks

Pull the relevant language-specific list from the repo structure:

• docs/threads/word-list/Common.md – Core tech vocabulary used across stacks
• docs/threads/word-list/JavaScript.md – Frontend and Node.js terminology
• docs/threads/word-list/Go.md – Systems programming and concurrency terms

These files provide the seed vocabulary that grounds the AI in actual industry usage rather than hallucinated terms.

3. Enrich with LLM Prompt Engineering

Feed the seed list plus the role description to the model with instructions to:

• Expand each term with synonyms, collocations, and idiomatic phrases used in tech contexts
• Rank by real-world frequency using GitHub search or StackOverflow corpus data
• Provide example sentences mirroring code reviews, sprint demos, or architecture discussions

This aligns with the "近义词对比 + 抽查" (synonym comparison + quizzing) pattern detailed in section 4. 词汇与语法 of docs/threads/part-1/7-ai.md.

4. Filter and Structure Output

Keep only the top N items (e.g., 30 terms) meeting a frequency threshold. Persist the result in JSON or CSV format for import into Anki, Quizlet, or Gemini's native flashcard interface.

5. Create a Feedback Loop

Implement the "词汇抽查" (vocabulary spot-check) loop. After each study session, the LLM quizzes the learner and automatically updates the list with new gaps, ensuring the vocabulary set remains dynamic and personally relevant.

Practical Implementation: Python and OpenAI Example

Below is a self-contained script that generates a custom tech-vocab list for a Frontend Engineer focusing on JavaScript. It reads the repository's seed list, calls OpenAI's gpt-4o-mini model, and outputs JSON ready for flashcard apps.

import os
import json
import pathlib
import openai  # pip install openai

# -------------------------------------------------

# 1.  Load seed list from the repository

# -------------------------------------------------

SEED_PATH = pathlib.Path(
    "docs/threads/word-list/JavaScript.md"
)  # Path within cloned repo

seed_terms = [
    line.strip()
    for line in SEED_PATH.read_text(encoding="utf-8").splitlines()
    if line and not line.startswith("#")
]

# -------------------------------------------------

# 2.  Build the LLM prompt

# -------------------------------------------------

role_desc = (
    "You are an English coach for a senior frontend engineer. "
    "Generate a targeted vocabulary list based on the following seed terms. "
    "For each term, provide:\n"
    "1. The term itself (keep original if already tech-specific).\n"
    "2. Two high-frequency synonyms or related phrases used in modern JS development.\n"
    "3. One concise example sentence that could appear in a code review or sprint demo.\n"
    "4. Approximate real-world frequency ranking (1 = most common on GitHub/StackOverflow).\n"
    "Return the result as a JSON array of objects."
)

prompt = f"""\
Seed terms:\n{', '.join(seed_terms)}\n\n{role_desc}
"""

# -------------------------------------------------

# 3.  Call the LLM

# -------------------------------------------------

openai.api_key = os.getenv("OPENAI_API_KEY")
response = openai.ChatCompletion.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": prompt}],
    temperature=0.7,
    max_tokens=2000,
)

# -------------------------------------------------

# 4.  Parse and persist the enriched list

# -------------------------------------------------

try:
    vocab_json = json.loads(response.choices[0].message.content)
except json.JSONDecodeError:
    vocab_json = []  # Handle parsing errors in production

out_path = pathlib.Path("frontend_js_vocab.json")
out_path.write_text(json.dumps(vocab_json, ensure_ascii=False, indent=2))
print(f"Enriched vocab list saved to {out_path}")

Adaptation note: Swap the SEED_PATH to docs/threads/word-list/Go.md or Common.md to target backend engineers or general tech roles.

No-Code Alternative Using Gemini

For a zero-code workflow, use the prompt strategy from docs/threads/part-1/7-ai.md:

1. Copy the contents of docs/threads/word-list/JavaScript.md (or your target seed file)
2. Paste into Gemini's chat with the following prompt:

Create flashcards about this material. Focus on high-frequency vocabulary, collocations, and sentence patterns that are useful in real conversations, not just rare difficult words.

3. Gemini returns flashcards exportable to its flashcard UI or downloadable as CSV for Anki import.

Maintaining Lists with the Vocabulary Absorption Loop

Sustained retention requires the "词汇抽查" loop described in the AI chapter. After initial study, run this follow-up prompt:

Save the vocabulary and expressions I marked as useful today. Three days later, test me on them with mixed formats: translation, fill-in-the-blank, and sentence creation.

Gemini or ChatGPT will automatically schedule the quiz, ensuring the list stays fresh. This implements the continuous feedback mechanism without manual spaced-repetition calculation.

Converting JSON to Anki CSV

If using the Python script output with Anki, convert the JSON to CSV format:

import csv
import json

with open('frontend_js_vocab.json') as f:
    data = json.load(f)

with open('frontend_js_vocab.csv', 'w', newline='', encoding='utf-8') as out:
    writer = csv.writer(out)
    writer.writerow(['Front', 'Back'])
    for item in data:
        back = f"{item['example']} (Synonyms: {', '.join(item['synonyms'])})"
        writer.writerow([item['term'], back])

Summary

• Seed with repository data: Use docs/threads/word-list/*.md files from byoungd/English-level-up-tips as the foundation for domain-specific terms.
• Enrich with AI: Feed seed lists to GPT-4o, Gemini, or Claude to generate synonyms, collocations, and workplace example sentences.
• Structure for retention: Export to JSON or CSV for import into Anki, Quizlet, or Gemini's flashcard system.
• Automate review: Implement the "词汇抽查" loop to have AI automatically quiz you on marked terms three days later.
• Target high-frequency terms: Prioritize words appearing in GitHub repos and StackOverflow over academic rarity.

Frequently Asked Questions

How do I choose which seed word bank to use from the repository?

Select the file matching your primary technology stack. Use docs/threads/word-list/JavaScript.md for frontend roles, docs/threads/word-list/Go.md for systems/backend, and docs/threads/word-list/Common.md for general tech communication applicable across domains. According to the repository structure in byoungd/English-level-up-tips, these files contain curated high-frequency terms specific to each ecosystem.

Can I use this workflow without knowing how to code?

Yes. The no-code approach leverages the prompts detailed in docs/threads/part-1/7-ai.md. Simply copy the contents of any word-list markdown file into Gemini or ChatGPT, then append the flashcard creation prompt. The AI will generate formatted cards you can export directly without writing Python scripts.

What is the "词汇抽查" loop and why is it important?

The "词汇抽查" (vocabulary spot-check) loop is a continuous feedback system described in the AI chapter where the LLM saves your marked terms and automatically generates quizzes after a set interval (typically three days). This ensures active recall and prevents the "learn once, forget immediately" problem common with passive vocabulary study.

How does the AI determine "real-world frequency" for technical terms?

When properly prompted, the LLM references its training data on GitHub repositories, StackOverflow discussions, and technical documentation to estimate frequency rankings. For higher accuracy, you can augment the prompt with specific corpus instructions: "Rank these terms by frequency of appearance in GitHub JavaScript repositories and StackOverflow questions from 2023-2024."

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}