∆ AI Decision Graph
Decision-first AI concepts

AI Decision Graph

Understand AI concepts by decisions, not definitions. See when to use RAG, agents, MCP, fine-tuning, workflows, evaluation, and the concepts around them.

Start with AI Agents Choose RAG or Fine-tuning Explore Graph
local graph preview RAG neighborhood
RAG Embedding Vector Database Chunking Reranking Hybrid Search Fine-tuning Long Context
Popular paths

Start from a product decision.

Agent products

Build an AI Agent

Understand when autonomy is useful, and when a deterministic workflow is safer.

 Integration

Understand MCP

Decide whether you need a protocol layer or simple tool/function calling.
Featured comparisons

High-intent AI choices.

Agent vs Workflow

Agent vs Workflow: The Practical Difference for AI Products

Use workflows when the path is known. Use agents when the next step depends on uncertain observations.

 MCP vs Function Calling

MCP vs Function Calling: When Each One Makes Sense

Use function calling for narrow in-app tools. Consider MCP when reusable tool and context servers become part of the product architecture.

 RAG vs Fine-tuning

RAG vs Fine-tuning: Which Should You Choose?

Choose RAG for private or changing knowledge. Choose fine-tuning for repeated behavior, style, or task consistency.

 RAG vs Long Context

RAG vs Long Context: When Is Retrieval Worth It?

Use long context for bounded source material and prototypes. Use RAG when the corpus grows, repeats, needs ranking, or needs citations.

 Vector Database vs Full-text Search

Vector Database vs Full-text Search: Which Retrieval Should You Use?

Use vector search for semantic similarity, full-text search for exact terms, and hybrid search when users need both.
Core concepts

The first decision nodes.

rag medium

RAG

Use RAG when the model needs private, updated, source-backed, or domain-specific knowledge.
agents high

Agent

Use an agent when the task needs adaptive planning across uncertain steps; use a workflow when the path is known.
agents medium

MCP

Use MCP when reusable tool and context integrations matter across clients; use direct function calling for a narrow in-app MVP.
rag high

Fine-tuning

Use fine-tuning when you need consistent behavior across many similar tasks, not when you only need new facts.
safety medium

Evaluation

Add evaluation before you optimize, scale, or trust an AI feature with important user outcomes.
rag medium

Vector Database

Use a vector database when semantic retrieval needs to scale beyond simple in-memory search.