AI Primitives includes six types: text generation for written content, image generation for visuals, code generation for software, audio/video generation for media, embedding generation for semantic search, and tool calling for AI that takes actions. The right choice depends on what output you need. Text generation handles most content automation. Embeddings enable search. Tool calling builds agents. Most AI systems combine multiple primitives, using text for reasoning, embeddings for retrieval, and tool calling for action.
You need 50 personalized emails, 200 product images, types for every API endpoint, and a training video that updates when processes change.
Doing it manually would take weeks. Your team is already stretched thin.
Or you describe what you need, and AI generates it in minutes. Same judgment, different scale.
AI primitives are the building blocks. What you build with them is up to you.
Part of Layer 2: Intelligence Infrastructure - Where AI capabilities live.
AI Primitives are the core capabilities that make AI useful: generating text, creating images, writing code, producing audio and video, creating embeddings for search, and calling tools to take actions. Every AI application combines one or more of these primitives.
Text generation gets the most attention, but embeddings quietly power every semantic search and RAG system. Tool calling is what separates chatbots from agents. Know all six, combine as needed.
Each primitive solves a different problem. Choosing wrong means building something that cannot do what you need.
Text | Image | Code | Audio/Video | Embeddings | Tool Calling | |
|---|---|---|---|---|---|---|
| Output Type | ||||||
| Primary Use | ||||||
| Maturity Level | ||||||
| Validation Need |
The right choice depends on what you are trying to produce. Most systems use multiple primitives together.
“I need to write emails, reports, or responses at scale”
Text generation handles any written content where you can describe what you want.
“I need product photos or marketing visuals without a photo shoot”
Image generation creates visuals from descriptions, enabling variations at scale.
“I need to generate types, tests, or boilerplate from specifications”
Code generation turns specifications into working code, automating tedious dev tasks.
“I need training videos or voiceovers that update when content changes”
Audio/video generation creates media from text, making updates simple.
“I need search that understands meaning, not just keywords”
Embeddings convert text to vectors, enabling semantic similarity matching.
“I need AI to query databases, call APIs, or take actions”
Tool calling lets AI decide when to invoke external functions based on context.
“I need all of the above for different parts of my system”
Most production AI systems combine multiple primitives for complete solutions.
Answer a few questions to get a recommendation.
AI primitives are not about replacing human work. They are about scaling human judgment. You encode what good output looks like, AI produces it at scale.
A task requires creating content, finding information, or taking action
Choose the primitive that produces the right output type
What took hours now takes minutes, at the quality you defined
When drafting 50 personalized outreach emails would take 16 hours...
That's a text generation problem - encode your criteria once, let AI apply them at scale.
When users search "refund policy" but miss the "Returns and Exchanges" doc...
That's an embedding problem - semantic search finds by meaning, not keywords.
When training videos need updating every time a process changes...
That's an audio/video generation problem - update the script, regenerate the video.
When the AI assistant guesses because it cannot check live data...
That's a tool calling problem - give AI tools to query real data before responding.
Which of these sounds most like your current situation?
These mistakes appear across all six primitives. Avoid them from the start.
Move fast. Structure data “good enough.” Scale up. Data becomes messy. Painful migration later. The fix is simple: think about access patterns upfront. It takes an hour now. It saves weeks later.
AI primitives are the fundamental building blocks that AI systems use to generate output. They include text generation for writing content, image generation for creating visuals, code generation for producing software, audio/video generation for media creation, embedding generation for semantic understanding, and tool calling for taking actions. Every AI application you use combines one or more of these primitives to accomplish its task.
Choose based on your output needs. Use text generation for emails, reports, and content at scale. Use image generation for product visuals, marketing assets, and design prototypes. Use code generation for automating development tasks. Use embeddings when you need search to understand meaning rather than just match keywords. Use tool calling when AI needs to take actions like querying databases or sending notifications.
Text generation creates new content by producing words that follow your instructions. Embedding generation converts existing content into numerical representations that capture meaning. Text generation outputs readable text. Embeddings output vectors (lists of numbers) that let you compare similarity between concepts. Use text generation to create content. Use embeddings to search and retrieve content.
Tool calling (also called function calling) lets AI decide when to invoke external functions like APIs, databases, or services. Without it, AI can only process and generate information. With tool calling, AI becomes an agent that can take actions. You need it when building AI assistants that query live data, update records, send emails, or perform any action beyond just generating text.
Yes, most sophisticated AI systems combine multiple primitives. A typical RAG (retrieval-augmented generation) system uses embeddings to find relevant documents and text generation to synthesize answers. An AI agent uses text generation for reasoning, embeddings for context retrieval, and tool calling to execute actions. The primitives are building blocks that compose into complete solutions.
The biggest mistakes across all primitives are skipping validation of AI output, using one primitive for tasks better suited to another, and ignoring consistency requirements. For text, validate facts against sources. For images, maintain brand consistency. For code, always test before deploying. For tool calling, add guardrails to prevent dangerous actions. Never trust AI output without verification.
Both take prompts and produce output, but they solve different problems. Text generation creates written content at scale, replacing hours of writing with minutes. Image generation creates visual content, replacing expensive photo shoots or design work. Text models are more mature and reliable. Image models require more prompt engineering for consistent results. Most businesses start with text generation.
AI primitives are the intelligence layer in larger systems. Text generation connects to prompt architecture for instruction design and output control for formatting. Embeddings connect to vector databases for storage and retrieval systems for search. Tool calling connects to agent orchestrators for complex workflows. The primitives provide the AI capability; other components provide structure and control.
Have a different question? Let's talk