AI & Machine Learning Templates
AI and machine learning architecture templates — RAG systems, AI agents, ML training pipelines, LLM apps, and MLOps flows. Turn a complex AI stack into a clear, shareable diagram.
11 templates
How LLMs Work
A clear map of how large language models turn a prompt into an answer — and the models and hardware behind them
Claude Code Architecture
How Claude Code reads your repository, calls tools through MCP, and edits code from the terminal
Codex Architecture
How an AI coding agent like Codex plans, writes and tests code inside a secure sandbox
AI Tool Ecosystem
Your AI stack as a living orbit — models, frameworks and vector stores revolving around the product they power
RAG Architecture Diagram
Map how retrieval-augmented generation grounds an LLM in your data with a vector database
AI Agent Architecture Diagram
Visualize the reasoning loop, tools, and memory that let an AI agent plan and act
ML Training Pipeline Diagram
Chart every stage from raw data to a trained, validated machine learning model
LLM Application Architecture Diagram
See how a production LLM app wires frontend, orchestration, model APIs, and guardrails
Recommendation System Architecture Diagram
Show how candidate generation, ranking, and filtering produce personalized recommendations
MLOps Pipeline Diagram
Trace the flow from training and CI/CD to deployment, monitoring, and retraining
Neural Network Architecture Diagram
Break down a feedforward neural network from input through hidden layers to output
About ai & ml diagrams
AI and machine learning diagrams explain systems that are hard to see from the outside. They show how a RAG app finds the right documents, how an agent picks a tool, or how a model reaches production. They turn models, vector stores and orchestration code into a picture that engineers and executives can both follow.
ML engineers and AI platform teams use them in design reviews and runbooks. Product teams use them to explain what an AI feature can and cannot see. Developer advocates and educators use explainer diagrams, such as How LLMs Work or Claude Code Architecture, to show how the pieces fit together.
A good AI architecture diagram separates the offline path from the online path. Indexing documents or training a model happens before any user arrives, while retrieval and inference happen on every request. Show where context comes from, such as retrieved chunks, memory or tool results. Show where guardrails and evaluation sit, and which model and vector store are in use. Loops matter too. Agents plan, act and observe, and MLOps pipelines retrain when drift appears, so draw the feedback path instead of a straight line.
Which template should you pick?
- Pick RAG Architecture Diagram when answers must be grounded in your own documents and you need to show embedding, vector search, retrieval and prompt assembly.
- Pick AI Agent Architecture Diagram when the system reasons, calls tools and draws on memory and knowledge across steps instead of answering in one pass.
- Pick LLM Application Architecture Diagram when documenting a production LLM app with prompt management, a semantic cache, safety guardrails and logging.
- Pick MLOps Pipeline Diagram over ML Training Pipeline Diagram when the model is already live and versioning, drift monitoring and retraining matter more than training steps.
- Pick How LLMs Work or AI Tool Ecosystem when the audience is non-technical and needs the landscape of models and tools, not the plumbing.
Or generate one from a prompt
- “RAG system for a support chatbot: help-center articles chunked and embedded into pgvector, hybrid search, a reranker, prompt assembly, GPT model and cited answers.”
- “AI travel-booking agent: planner LLM, tools for flight search, hotel API and calendar, short-term memory, a user preference store, and human approval before payment.”
- “MLOps pipeline for a fraud model: feature store, training job, model registry, canary deployment, drift monitoring, and automatic retraining when precision drops.”
Paste any of these into the AI diagram generator to get an editable, animated diagram in seconds.
Tips for customizing ai & ml templates
- Split RAG into two paths: ingestion (chunk, embed, index) runs ahead of time, while retrieval runs on every query. Label which is which.
- Name the real components, such as the chat model, embedding model and vector database, and swap icons for their logos so the diagram matches your stack.
- Add the nodes that show control: evaluation, guardrails, human review and logging. That is where reviewers look for risk.
- For agent loops, try the Cycle style so plan, act and observe read as a loop rather than a list.
- Explaining a paper or a long design doc? Paste it into the Text to Visuals tab and choose from the visuals it suggests.
Learn more: How to turn text into an infographic