Vocabulary for Talking About AI and Machine Learning at Work

AI is now part of almost every engineering conversation, whether you build models or simply integrate an API. To take part confidently, you need the right vocabulary and the ability to use it naturally in meetings. This guide covers the core terms, common collocations, and example sentences you can borrow directly.

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Core Concepts

Term	Meaning
Model	A program that learns patterns from data to make predictions.
Training	The process of teaching a model using data.
Inference	Using a trained model to produce an output (a “prediction”).
Dataset	The collection of examples used for training or testing.
Parameters / weights	The internal numbers a model learns.
Fine-tuning	Adapting a pre-trained model to a specific task.
Prompt	The input text given to a language model.
Token	A chunk of text the model processes (roughly a word-part).

“We’re not training a model from scratch — we’re fine-tuning an existing one on our support tickets.”

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Talking About Quality

Teams obsess over how well a model performs. Know these terms:

• Accuracy — how often the model is correct.
• Precision — of the items flagged positive, how many truly are.
• Recall — of the truly positive items, how many were found.
• Hallucination — when a language model produces confident but false information.
• Drift — when model performance degrades over time as the real world changes.
• Benchmark — a standard test used to compare models.

“Precision is high, but recall is poor — we’re missing a lot of real fraud cases.” “The model is hallucinating product names that don’t exist, so we’ve added a retrieval step.”

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The Language of Large Language Models

Term	Meaning
LLM	Large language model.
Context window	How much text the model can consider at once.
Prompt engineering	Crafting inputs to get better outputs.
RAG	Retrieval-augmented generation — feeding the model relevant documents.
Embedding	A numerical representation of text used for search.
Temperature	A setting controlling how random the output is.
Guardrails	Rules that constrain what the model can output.

“We’re using RAG so the model answers from our own docs rather than its training data.” “Lower the temperature if you want more deterministic, consistent answers.”

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Verbs You’ll Hear Constantly

• to train a model
• to fine-tune on a dataset
• to deploy a model to production
• to evaluate against a benchmark
• to prompt the model
• to ground the answer in real data
• to serve inference requests

“Once we’ve evaluated it against the benchmark, we’ll deploy it behind a feature flag.”

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Useful Phrases for Meetings

When you want to raise a concern:

“My concern is that the training data may not represent our actual users.” “How confident are we in these accuracy numbers? What’s the size of the test set?”

When you want to manage expectations:

“This is a probabilistic system, so it will occasionally get things wrong — we need a human-in-the-loop for high-stakes cases.”

When you propose an approach:

“Rather than fine-tuning, I’d suggest we start with prompting and RAG — it’s cheaper to iterate on.”

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Words People Often Confuse

Often confused	Clarification
AI vs ML	ML is one approach to building AI.
Training vs inference	Training builds the model; inference uses it.
Parameter vs hyperparameter	Parameters are learned; hyperparameters are set by you.
Supervised vs unsupervised	Supervised uses labelled data; unsupervised finds patterns without labels.

Getting these distinctions right signals that you understand the field, not just the buzzwords.

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Hedging Language for Uncertainty

AI outputs are uncertain, and English has precise ways to express that:

• “The model tends to struggle with long documents.”
• “It occasionally produces incorrect citations.”
• “Results vary depending on the prompt.”
• “We can’t guarantee a correct answer every time.”

Avoid absolute claims like “the AI is always right” — they damage your credibility with technical and non-technical stakeholders alike.

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A Quick Self-Test

Try explaining each of these in a single sentence: hallucination, fine-tuning, context window, recall. If you can do that clearly, you can hold your own in almost any AI conversation at work.

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With this vocabulary you can move from nodding along to actively shaping AI discussions. Use the example sentences as templates, label your uncertainty honestly, and keep the distinctions between training and inference, precision and recall, crisp. Speaking precisely about AI is itself a competitive advantage in today’s engineering teams.

Navigating the AI Landscape: A Practical Approach

Let’s face it – “AI” can feel like a tangled web of jargon. But understanding the core concepts – from model training to prompt engineering – is crucial for effective collaboration and decision-making within any organization exploring machine learning. This post aims to equip you with the essential vocabulary and phrasing, focusing on practical applications rather than theoretical depths. We’ll cover key terms like models, training, inference, prompts, evaluation, and importantly, how to discuss them confidently in meetings and code reviews. Our goal isn’t to turn you into an AI expert, but to give you the tools to participate meaningfully in conversations about this rapidly evolving field.

In Practice: Translating Concepts into Actionable Language

Let’s consider a common scenario: a junior developer presenting a new model to the team during a code review. Instead of simply saying “I trained a model,” they could say, “I’ve implemented a transformer model using PyTorch. The training data consisted of [briefly describe data] and we utilized an Adam optimizer with a learning rate of 0.001. We’re currently running inference on the GPU to generate predictions based on user prompts.” Notice how they’ve moved beyond just stating what happened and started describing how it was done, focusing on key aspects relevant to the review – model type, data, optimization, and inference method. A more direct critique might be: “The current inference speed is a bottleneck; we should explore quantization techniques to reduce latency.” This demonstrates an understanding of performance considerations – vital when discussing AI projects.

Another example: during a project kickoff meeting, the team needs to discuss prompt design for a chatbot. Instead of saying “We need better prompts,” they could say, “Let’s focus on crafting clear and specific prompts that guide the model towards generating desired outputs. We should experiment with different prompting techniques – like few-shot learning – to improve accuracy and consistency.” This approach highlights the importance of a strategic approach to prompt engineering, demonstrating an awareness of best practices. It also invites discussion about experimentation and iterative refinement.

Furthermore, when discussing evaluation, it’s crucial to move beyond simply stating “the model performs well.” A more productive conversation would be: “We’re evaluating the model’s performance using metrics like F1-score and precision/recall on a held-out test set. We’ll also conduct human-in-the-loop evaluation to assess subjective quality.” This demonstrates a rigorous approach to measuring success, acknowledging potential limitations, and incorporating qualitative feedback.

Here’s a simple CLI example for generating an image using Stable Diffusion:

python scripts/generate_image.py --prompt "A majestic wolf howling at the moon, photorealistic" --width 512 --height 512 --num_inference_steps 50

This command uses the generate_image.py script (likely Python) and passes several parameters to Stable Diffusion: a text prompt describing the desired image, dimensions for the output, and the number of inference steps – all terms you’d want to understand when discussing AI model generation.

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