DEV Community: Anushka Gupta

RAG vs Agentic RAG : Explained with Real-World Examples

Anushka Gupta — Mon, 13 Jul 2026 16:27:59 +0000

Imagine you ask an AI:

"What is my company's leave policy?"

There are two ways the AI can answer:

It guesses based on what it learned during training.
It opens your company's HR documents, finds the correct policy, and then answers.

The second approach is Retrieval-Augmented Generation (RAG).

Now imagine instead of just looking up one document, the AI:

Decides which documents to search.
Realizes it needs more information.
Searches multiple sources.
Compares conflicting information.
Uses a calculator if needed.
Asks you a follow-up question if your request is ambiguous.
Verifies its answer before responding.

That's Agentic RAG.

Why Was RAG Needed?

Large Language Models (LLMs) like ChatGPT are trained on enormous datasets.

But they have limitations:

They don't know your private documents.
They don't know today's latest information unless connected to external sources.
They sometimes hallucinate (confidently generate incorrect information).

Imagine asking:

"What's the refund policy in my company's employee handbook?"

The LLM has never seen your handbook.

Without RAG it might answer:

"Most companies allow refunds within 30 days..."

That sounds reasonable...but could be completely wrong.

What is RAG?

Retrieval-Augmented Generation (RAG) is a technique that allows an AI model to retrieve relevant information from external knowledge sources before generating an answer.

Instead of relying only on its training, the AI first looks up the information, then writes a response using what it found.

Think of it like an open-book exam instead of a memory test.

Everyday Analogy :

Imagine you're a chef.

Someone asks:

"How many grams of salt does Grandma's lasagna recipe use?"

Option 1:

You guess.

Option 2:

You open Grandma's recipe book,
read the recipe,
then answer.

RAG is Option 2.

How RAG Works

Suppose your company has thousands of PDF files.

HR Policy.pdf
Employee Handbook.pdf
Travel Policy.pdf
Insurance Guide.pdf
IT Security.pdf

Now someone asks:

"How many casual leaves do employees get?"

The workflow looks like this:

User Question (Query)
      │
      ▼
Convert question into embeddings
      │
      ▼
Search Vector Database
      │
      ▼
Retrieve most relevant documents (Context)
      │
      ▼
Send retrieved content + question
to the LLM (Query + Context)
      │
      ▼
Generate final answer

The AI doesn't search every document. Instead, it retrieves only the most relevant ones.

Advantages of RAG

Up-to-date Information
No retraining needed. Just update documents.
Private Knowledge
Works with company documents.
Reduced Hallucination
Answers come from retrieved evidence.
Easy Maintenance
Updating a PDF updates the AI's knowledge.
Lower Cost
No need to retrain expensive models.

Limitations of RAG

RAG is powerful but still limited.

Suppose you ask:

"Compare all cloud providers, recommend one, calculate the yearly cost, and prepare a migration strategy."

Simple RAG retrieves documents.
It doesn't think step by step.
It retrieves once.
Answers once.

That's where Agentic RAG comes in.

You ask:

"I'm taking my parents out for dinner. Recommend a restaurant within ₹1500, serving vegetarian food, within 5 km, and book a table for 7 PM."

Traditional RAG :

RAG searches restaurant documents or reviews and replies:

Restaurant A
Restaurant B
Restaurant C

Maybe it summarizes reviews.

Done.

It doesn't:

Check your budget
Filter by distance
Check table availability
Make a booking

It simply retrieved information.

Agentic RAG :

The AI thinks like this:

Step 1: Find vegetarian restaurants
↓
Step 2: Filter within 5 km
↓
Step 3: Check average pricing
↓
Step 4: Check table availability
↓
Step 5: Book a table
↓
Step 6: Send confirmation

Here the AI is planning and taking actions, not just retrieving information.

What is Agentic RAG?

Agentic RAG extends traditional RAG by giving the AI decision-making capabilities.

Instead of following a fixed retrieve-then-answer pipeline, the AI acts like an intelligent agent that can plan, reason, choose tools, retrieve information multiple times, and validate its own results before responding.

Agentic RAG Workflow :

User Question
      │
      ▼
Understand Goal
      │
      ▼
Plan Steps
      │
      ▼
Retrieve Information
      │
      ▼
Need More?
     Yes
      │
Retrieve Again
      │
Use Tools
      │
Reason
      │
Validate
      │
Generate Final Answer

Notice retrieval can happen multiple times.

Final Takeaway

Retrieval-Augmented Generation (RAG) make LLMs far more useful by allowing them to access external knowledge instead of relying solely on what they memorized during training. It reduces hallucinations, keeps responses current, and enables AI to answer questions using private or domain-specific data.

Agentic RAG takes this a step further. Rather than following a single retrieve-and-answer pipeline, it behaves like an intelligent agent: it plans, retrieves information iteratively, uses tools, reasons through complex tasks, and validates its work before producing an answer.

A simple way to remember the difference is:

RAG helps an AI know more.
Agentic RAG helps an AI think and do more

npm vs pnpm: The Package Manager Battle Every React Developer Should Know

Anushka Gupta — Sun, 05 Jul 2026 20:41:50 +0000

If you've started learning React or Node.js, you've probably come across commands like npm install or pnpm install. Both npm and pnpm are package managers, they help you install and manage the libraries your project depends on.

Think of them as delivery services for your code. Need React? Axios? Tailwind CSS? Your package manager fetches and installs them for you.

What is npm?

npm (Node Package Manager) is the default package manager that comes with Node.js. It's widely used and works great for most projects.

npm install

What is pnpm?

Interestingly, pnpm doesn't officially stand for a fixed expansion. The name is commonly interpreted as: Performant Node Package Manager.
pnpm is a faster and more disk-efficient alternative to npm. Instead of downloading the same package multiple times for different projects, pnpm stores it once and reuses it wherever needed.

pnpm install

The Key Difference :

The main difference between npm and pnpm is how they install and manage dependencies. Both can be used to create and manage React projects, but pnpm is generally faster and more space-efficient.

Imagine you have two projects:

Project A uses React and Axios
Project B uses React only

With npm, each project keeps its own copy of the dependencies.

Project A
node_modules/
    react
    axios

Project B
node_modules/
    react

This duplicates packages across projects.

With pnpm, packages are stored once in a global store and linked to projects when needed. This saves both installation time and disk space.

Global Store
react
axios
lodash

Each project's node_modules contains links to these packages instead of full copies.

Project A
node_modules -> links

Project B
node_modules -> links

**
Why Developers Love pnpm
**
✅ Faster installs

✅ Less disk usage

✅ Better dependency management

✅ Excellent support for monorepos

A Real-World Example

Suppose Project B doesn't have Axios listed in its package.json, but your code tries to use it:

import axios from "axios";

With pnpm, you'll immediately get an error because Axios wasn't declared as a dependency.

Error: Cannot find module 'axios'

Here, your project can only access packages that are explicitly listed in its own package.json

While this may seem strict, it actually prevents the classic "works on my machine" problem and makes projects more reliable for teams.

The key thing to understand is that pnpm's global store is not the same as your project's dependencies.

With npm, the behavior is different because dependencies can sometimes be accidentally accessible.

Suppose Project B doesn't have Axios in its package.json:

{
  "dependencies": {
    "react": "^19.0.0"
  }
}

Now, when you do :
import axios from "axios";
What can happen?
If another package in node_modules already installed Axios as its own dependency, your code might still work with npm.

Project B
│
├── package.json
│   └── react
│
└── node_modules
    ├── react
    ├── some-library
    │   └── axios
    └── ...

Your application may successfully import axios even though you never declared it.

The problem: Another developer or your CI/CD pipeline may install a slightly different dependency tree where Axios isn't present at that location. Suddenly, the same code fails.

This leads to the classic:

✅ Works on my machine
❌ Fails on yours

Final Thoughts

npm is reliable, popular, and perfectly fine for many projects. But if you're starting a new React application, pnpm offers faster installs, better dependency isolation, and significant disk space savings.

That's why many modern development teams are making the switch to pnpm.