Module Focus
English for Special Purposes
AI Development English
High-level technical English for AI engineers, researchers, product managers, data specialists, and safety teams.
- 8 modules
- 42 field terms
- Interactive practice
Printable Curriculum
Download the full materials
Web Practice Lab
Rehearse the language, response, and decision
Work through a three-step sequence: identify the field language, choose the strongest response, then select the next controlled decision move.
Guided Decision Lab
Jargon Flashcard
Answer Rationale
Why the strongest phrase fits
Choose an option to see the workplace rationale.
Dialogue Coach
Model line
Language notes
Progress
Practice checklist
0 of 4 complete
Student PDF in Web Form
Module map
Speaking the AI Development Stack
AI teams use a layered vocabulary: model, data, prompt, retrieval, tools, serving, evaluation, monitoring, and product experience. Learners need to locate a problem in the stack before they can discuss it clearly.
LLM, Transformer, Parameter, Checkpoint
LLMs, Transformers, Tokens, and Context
High-level AI communication often depends on explaining what the model sees: tokens, messages, context window, instructions, examples, retrieved text, and tool results.
Foundation model, Multimodal, Prompt, System prompt
Data, Datasets, Labels, and Leakage
AI systems are shaped by data quality. Teams need precise language for dataset splits, annotation guidelines, leakage, imbalance, representativeness, and privacy constraints.
Few-shot, Context window, Token, Temperature
Retrieval, Embeddings, Vector Search, and RAG
Many production AI apps combine retrieval with generation. Learners need to discuss chunking, embeddings, vector stores, recall, reranking, grounding, citations, and retrieval misses.
Embedding, Vector store, Chunking, Reranker
Fine-Tuning, Alignment, and Adaptation
Teams often confuse prompt changes, RAG, fine-tuning, adapters, supervised fine-tuning, preference tuning, and RLHF. The language goal is to recommend the right adaptation method for the problem.
RAG, Grounding, Fine-tuning, SFT
Evaluation, Benchmarks, and Regression
AI teams need language for uncertainty. 'It looks better' is not enough. Learners need to discuss offline evals, online evals, golden sets, human review, model-graded evals, regression, pass rate, and confidence.
RLHF, DPO, LoRA, Adapter
Inference, Latency, Cost, and Deployment
AI development is also systems engineering. Learners need vocabulary for inference paths, throughput, batching, caching, rate limits, GPUs, quantization, streaming, fallbacks, and SLOs.
Eval, Benchmark, Golden set, Regression
Safety, Security, Privacy, and Governance
AI teams must discuss risk precisely: hallucination, prompt injection, jailbreaks, PII, data retention, bias, harmful output, policy enforcement, audit logs, and human-in-the-loop review.
Pass rate, LLM-as-judge, Inference, Latency
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