Building Real Apps With the Claude API — Tool Use, RAG, and Agent Patterns Explained
Source: Dev.to
Overview
Calling the Claude API is easy: put a prompt in messages.create and get an answer back.
But that alone doesn’t make a product. To build a real app you need three more things: Tool Use, RAG, and Agent/Workflow patterns.
Tool Use — Giving Claude Hands
Base Claude only generates text. Tool Use lets it call external functions.
[
{
"name": "get_apartment_price",
"description": "Look up apartment prices for a district",
"input_schema": {
"type": "object",
"properties": {
"district": { "type": "string" },
"year": { "type": "integer" }
},
"required": ["district"]
}
}
]
Flow
- User question →
- Claude picks tools →
- Your code executes →
- Return
tool_result→ - Claude writes final response.
RAG — Making Claude Know What It Doesn’t
RAG (Retrieval‑Augmented Generation) injects external data into prompts.
Pipeline
- Chunking
- Embedding
- Retrieval (BM25 + vector search)
- Re‑ranking
- Context injection
This turns vague model output into data‑grounded answers.
Agents and Workflows — Chaining Multiple Steps
Three workflow patterns:
- Parallelization – run independent tasks at the same time
- Chaining – one step’s output feeds the next
- Routing – classify inputs and send them to specialized paths
Practical distinction
- Workflows are predictable and stable.
- Agents are flexible but less predictable.
Start with workflows. Add agent behavior after guardrails are in place.
How This Becomes a Real App
- Resume analysis service – run technical‑skills, career‑trajectory, and culture‑fit checks in parallel.
- Fortune‑telling app (saju) – connect deterministic calendar/element tools, retrieve interpretation knowledge via RAG, then chain base analysis → detailed interpretation → recommendations.
- Real‑estate analysis – combine price APIs (Tool Use), policy/news retrieval (RAG), and parallel analysis pipelines.
The API call is just the beginning. Tool Use gives Claude hands, RAG gives it memory, and workflows give it a brain.