DEV Community

Cover image for Tool Use vs RAG: picking the right approach
Gantz profile image Gantz AI
Gantz AI for Gantz

Posted on

Tool Use vs RAG: picking the right approach

#agents #ai #llm #rag

Two ways to make AI smarter: give it knowledge (RAG) or give it abilities (Tool Use).

I see people pick the wrong one all the time. Let's clear this up.

What RAG does

RAG (Retrieval Augmented Generation) gives AI access to knowledge.

User: "What's our refund policy?"

System:
1. Search knowledge base for "refund policy"
2. Find relevant documents
3. Inject into AI context
4. AI answers based on retrieved content

AI: "According to our policy, refunds are available within 30 days..."
Enter fullscreen mode Exit fullscreen mode

RAG is about knowing things. You have documents, and AI needs to reference them.

What Tool Use does

Tool Use gives AI the ability to take actions.

User: "Process a refund for order #123"

System:
1. AI decides to call refund_order tool
2. Tool executes against real systems
3. Refund is actually processed
4. AI confirms completion

AI: "Done. I've processed a $49.99 refund for order #123."
Enter fullscreen mode Exit fullscreen mode

Tool Use is about doing things. You have systems, and AI needs to interact with them.

The core difference

RAG Tool Use
Purpose Answer questions Take actions
Data flow Documents → AI AI → External systems
Output Information Effects
Example "What is X?" "Do X"

RAG = AI reads stuff
Tool Use = AI does stuff

When to use RAG

You have static knowledge

Company policies, documentation, historical data, FAQs.

User: "What are the system requirements?"
→ RAG retrieves from docs
→ AI summarizes requirements
Enter fullscreen mode Exit fullscreen mode

Answers exist in documents

The information already exists somewhere. AI just needs to find and present it.

User: "What did we decide in last month's meeting?"
→ RAG retrieves meeting notes
→ AI extracts the decision
Enter fullscreen mode Exit fullscreen mode

No action needed

User wants information, not changes. Read-only.

User: "How do I configure the API?"
→ RAG retrieves setup guide
→ AI explains the steps
Enter fullscreen mode Exit fullscreen mode

Volume of context

You have more documents than fit in context. RAG retrieves only relevant parts.

User: "Find the section about authentication"
→ RAG searches 1000 pages
→ Returns the 2 relevant pages
→ AI answers from those
Enter fullscreen mode Exit fullscreen mode

When to use Tool Use

Real-time data needed

Information changes constantly. Documents would be stale.

User: "What's my account balance?"
→ Tool calls banking API
→ Returns current balance
Enter fullscreen mode Exit fullscreen mode

Actions required

User wants something to happen, not just information.

User: "Send an email to the team"
→ Tool composes and sends email
→ Email actually goes out
Enter fullscreen mode Exit fullscreen mode

Computation needed

The answer requires calculation or processing.

User: "How much would 15% discount save on my cart?"
→ Tool fetches cart total
→ Tool calculates discount
→ Returns computed savings
Enter fullscreen mode Exit fullscreen mode

External systems

Data lives in databases, APIs, or services — not documents.

User: "Show me orders from last week"
→ Tool queries database
→ Returns live results
Enter fullscreen mode Exit fullscreen mode

Use both together

Often, you need both. This is where it gets powerful.

Example: Customer support bot

User: "I want to return this item. What's the process and can you start it?"

Step 1 - RAG:
→ Retrieves return policy
→ AI explains the 30-day window, conditions, etc.

Step 2 - Tool Use:
→ AI calls get_order tool to check purchase date
→ Confirms item is eligible

Step 3 - Tool Use:
→ AI calls initiate_return tool
→ Return process started

AI: "Your item qualifies for return (purchased 12 days ago).
I've initiated the return — you'll receive a shipping label via email."
Enter fullscreen mode Exit fullscreen mode

RAG provided the policy knowledge. Tools took the action.

Example: Research assistant

User: "Find recent papers on transformer efficiency and summarize the key findings"

Step 1 - Tool Use:
→ AI calls search_arxiv tool
→ Finds 10 recent papers

Step 2 - RAG:
→ Papers loaded into context
→ AI reads and analyzes

Step 3 - Tool Use:
→ AI calls save_summary tool
→ Saves the analysis

AI: "I found 10 papers from the last month. Key findings:
1. Sparse attention reduces compute by 40%...
[Summary saved to your notes]"
Enter fullscreen mode Exit fullscreen mode

Tools did the searching and saving. RAG handled the reading.

Decision framework

Ask yourself:

1. Does the answer exist in documents?

Yes → RAG
No → Tool Use

2. Does the user want information or action?

Information → Probably RAG
Action → Tool Use

3. Is the data static or dynamic?

Static (rarely changes) → RAG
Dynamic (changes frequently) → Tool Use

4. Is reading enough, or does something need to happen?

Reading → RAG
Something happens → Tool Use

Quick reference

User wants... Use
"What is our policy on X?" RAG
"Update my policy to X" Tool Use
"Explain how Y works" RAG
"Make Y do Z" Tool Use
"Find information about X" RAG (or Tool for live data)
"Create/Send/Delete X" Tool Use
"What did document say about X?" RAG
"What is the current X?" Tool Use

Common mistakes

Mistake 1: RAG for live data 

User: "What's the stock price of Apple?"

Wrong: RAG over old financial documents
Right: Tool that calls stock API
Enter fullscreen mode Exit fullscreen mode

RAG can't give you real-time data. It only knows what's in your documents.

Mistake 2: Tool Use for static knowledge 

User: "What are your business hours?"

Wrong: Tool that queries a database
Right: RAG over FAQ document
Enter fullscreen mode Exit fullscreen mode

Don't over-engineer. If it's in a document, use RAG.

Mistake 3: Only using one

Most real applications need both.

  • Support bot: RAG for policies + Tools for actions
  • Research assistant: Tools for search + RAG for reading
  • Business assistant: RAG for docs + Tools for data

Mistake 4: RAG for tiny context

If your entire knowledge base fits in context, you might not need RAG.

# If you only have 10 pages of docs
# Just include them in the system prompt
# No retrieval needed
Enter fullscreen mode Exit fullscreen mode

RAG adds complexity. Only use it when you have too much to fit in context.

Implementation

RAG stack 

Documents → Chunking → Embeddings → Vector DB → Retrieval → AI
Enter fullscreen mode Exit fullscreen mode

Tools: Pinecone, Weaviate, Chroma, pgvector

Tool Use stack 

AI → MCP/Function Calling → Tool Server → External Systems
Enter fullscreen mode Exit fullscreen mode

Tools: Gantz Run, LangChain, custom MCP servers

Both together 

┌─────────────────────────────────────┐
│              AI Agent               │
├─────────────────────────────────────┤
│                                     │
│   ┌─────────────┐ ┌─────────────┐   │
│   │    RAG      │ │  Tool Use   │   │
│   │             │ │             │   │
│   │  Knowledge  │ │   Actions   │   │
│   │  retrieval  │ │  execution  │   │
│   └──────┬──────┘ └──────┬──────┘   │
│          │               │          │
└──────────┼───────────────┼──────────┘
           │               │
           ▼               ▼
    ┌────────────┐  ┌────────────┐
    │ Vector DB  │  │ MCP Server │
    │ (Docs)     │  │ (Tools)    │
    └────────────┘  └────────────┘
Enter fullscreen mode Exit fullscreen mode

Summary

Use RAG when:

  • Information exists in documents
  • User wants to know things
  • Data is static
  • Reading is enough

Use Tool Use when:

  • Data is live/real-time
  • User wants actions taken
  • External systems involved
  • Something needs to happen

Use both when:

  • Complex workflows
  • Knowledge + action needed
  • Real-world applications

Don't overthink it. Start with what the user needs:

  • Need to know? → RAG
  • Need to do? → Tools
  • Need both? → Use both

How do you decide between RAG and tools in your projects?

Top comments (0)