AI Terminology Skill

Overview

This skill ensures consistent, accurate usage of AI/ML terminology throughout the FTE+AI documentation, making content accessible to R&D audiences with varying AI expertise.

Core Terminology

AI/ML Fundamentals

Artificial Intelligence (AI)

• Definition: Computer systems that can perform tasks typically requiring human intelligence
• Usage: Use when discussing broad capabilities (reasoning, learning, problem-solving)
• Context for R&D: "AI can automate repetitive coding tasks and augment developer productivity"

Machine Learning (ML)

• Definition: Subset of AI where systems learn from data without explicit programming
• Usage: Use when discussing models trained on data
• Context: "ML models can predict code defects based on historical patterns"

Large Language Models (LLMs)

• Definition: AI models trained on vast text data to understand and generate human language
• Examples: GPT-4/5, Claude, Gemini, Qwen-Next, GLM-4.6, MiniMax-M2
• Usage: Use when discussing text generation, code completion, or conversational AI
• Context: "LLMs like GPT-4 can generate documentation from code comments"

Generative AI

• Definition: AI systems that create new content (text, code, images)
• Usage: Use when discussing content creation capabilities
• Context: "Generative AI can create test cases and mock data for QA"

Model Types & Architectures

Foundation Models

• Pre-trained models that can be adapted for multiple tasks
• Examples: GPT, BERT, T5
• Usage: When discussing base models before fine-tuning

Fine-tuned Models

• Models adapted for specific tasks or domains
• Usage: "A fine-tuned model trained on your codebase"

Multimodal Models

• Models that handle multiple input types (text, image, audio)
• Examples: GPT-4V, Gemini
• Usage: When discussing vision + language capabilities

AI Agents & Systems

AI Agent

• Definition: Autonomous system that perceives, decides, and acts to achieve goals
• Usage: Use for systems with agency and decision-making
• Context: "Deploy an AI agent to automatically triage customer support tickets"

Prompt

• Definition: Instructions or input given to an AI model
• Usage: Describe user input to LLMs
• Context: "Craft clear prompts to get accurate code generation"

Prompt Engineering

• Definition: The practice of designing effective prompts to get desired outputs
• Usage: When discussing optimization of AI interactions
• Context: "Effective prompt engineering increases AI output quality by 40%"

Context Window

• Definition: The amount of text an LLM can process at once
• Usage: When discussing model limitations
• Context: "GPT-4 has a 128K token context window, enough for large codebases"

Token

• Definition: Basic unit of text processing (roughly 4 characters or 0.75 words)
• Usage: When discussing model capacity or costs
• Context: "Processing 1M tokens costs approximately $10 with GPT-4"

Training & Learning

Training

• Process of teaching an ML model using data
• Usage: "Training a model on your company's documentation"

Fine-tuning

• Adapting a pre-trained model for specific tasks
• Usage: "Fine-tune GPT-4 on your API documentation"

Retrieval-Augmented Generation (RAG)

• Definition: Technique where AI retrieves relevant info before generating responses
• Usage: When discussing knowledge-enhanced AI systems
• Context: "RAG enables AI to access your latest documentation without retraining"

Embeddings

• Definition: Numerical representations of text that capture semantic meaning
• Usage: When discussing semantic search or similarity
• Context: "Use embeddings to find similar code snippets in your repository"

Vector Database

• Definition: Database optimized for storing and searching embeddings
• Examples: Pinecone, Weaviate, Qdrant
• Usage: When discussing RAG implementations

Model Performance

Hallucination

• Definition: When AI generates false or fabricated information
• Usage: Address reliability concerns
• Context: "Implement verification steps to catch AI hallucinations"

Accuracy

• Percentage of correct predictions
• Usage: "The model achieves 95% accuracy on code classification"

Latency

• Response time from input to output
• Usage: "Sub-second latency is critical for code completion"

Throughput

• Number of requests processed per unit time
• Usage: "The system handles 1000 API calls per minute"

Common AI Tasks

Classification

• Categorizing inputs into predefined categories
• Example: "Bug triage: critical, high, medium, low"

Generation

• Creating new content
• Example: "Generate unit tests from function signatures"

Summarization

• Condensing long text into key points
• Example: "Summarize meeting notes into action items"

Translation

• Converting between languages or formats
• Example: "Translate Python to TypeScript"

Sentiment Analysis

• Determining emotional tone
• Example: "Analyze customer feedback sentiment"

Enterprise AI Terms

API (Application Programming Interface)

• How to interact with AI services programmatically
• Usage: "Integrate AI via REST API calls"

SDK (Software Development Kit)

• Pre-built libraries for AI integration
• Usage: "Use OpenAI's Python SDK for faster development"

Inference

• Running a trained model to get predictions
• Usage: "Real-time inference on production data"

Model Deployment

• Making a trained model available for use
• Usage: "Deploy the model to AWS Lambda"

Cost & Resource Terms

API Cost

• Pay-per-use pricing for AI services
• Usage: "GPT-4 costs $30 per 1M input tokens"

Self-hosted vs. Cloud-hosted

• Self-hosted: Run models on your own infrastructure
• Cloud-hosted: Use third-party AI services
• Usage: Compare deployment options

Vendor Lock-in

• Dependency on specific AI provider
• Usage: Risk assessment discussions

Terminology Guidelines

Consistency Rules

1. First use: Always define acronyms: "Large Language Model (LLM)"
2. Subsequent uses: Use short form: "The LLM generates..."
3. Product names: Keep official capitalization: "GitHub Copilot", "OpenAI GPT-4"
4. Avoid mixing: Don't alternate between "AI agent" and "intelligent agent"

Simplification for Non-Technical Audiences

Common Mistakes to Avoid

❌ "Artificial intelligence (AI)" → ✓ "Artificial Intelligence (AI)"
❌ "GPT-4 model" → ✓ "GPT-4" (GPT already means model)
❌ "AI/ML agent" → ✓ "AI agent" (ML is subset of AI)
❌ "Machine learning algorithm" → ✓ "Machine learning model" (in context of LLMs)

Glossary Template

Maintain a project-wide glossary with:

• Term: Official name
• Definition: Clear explanation
• Example: Real-world usage in FTE+AI context
• Related terms: Cross-references
• See also: Links to detailed docs

When to Use Technical vs. Business Language

Technical Documentation (Developers):

• Use precise terms: "tokens", "context window", "embeddings"
• Include specifications: "8K context window", "gpt-4-0125-preview"
• Reference APIs and SDKs directly

Business Documentation (Executives):

• Use analogies: "AI's memory" instead of "context window"
• Focus on outcomes: "reduces time by 50%" vs. "processes 100K tokens/sec"
• Minimize acronyms

Hybrid Documentation (R&D Managers):

• Define terms inline: "The context window (AI's working memory) limits..."
• Balance: Technical accuracy + business value
• Use sidebars for deeper technical details