Building a RAG-based Travel Recommender: Learning Journey

Session 1: Planning

Ask Claude:

"Can you explain the overall RAG system architecture?"
"What components will we need to build?"
"How will data flow through our system?"
"What development environment do we need?"
"Which APIs and tools should we prepare?"
"How should we structure our project?"
"What order should we build components in?"
"What are potential challenges we might face?"
"How can we test each component?"

You should understand:

✓ The complete system architecture
✓ Each component's role
✓ How components interact

Checkpoint: Can you:

✓ List all required tools and APIs?
✓ Explain where each tool fits in?
✓ Draw a system diagram?
✓ Create a development timeline?
✓ Identify critical dependencies?
✓ Plan your testing approach?

Session 2: Understanding Core Concepts

1. Understanding Vector Databases

Ask Claude:

"What is a vector database in simple terms?"
"How is it different from a regular database?"
"Can you explain vector similarity with a real-world example?"

You should understand:

✓ Why regular databases can't find "similar" items easily
✓ Why we need vector databases for recommendations
✓ What makes items "similar" in a vector database

2. Understanding Embeddings

Ask Claude:

"What are embeddings and why do we need them?"
"How do embeddings capture meaning?"
"Can you explain embeddings using a simple analogy?"

You should understand:

✓ How text gets converted to numbers
✓ Why similar meanings have similar numbers
✓ How embeddings help find similar items

3. Understanding Weights

Ask Claude:

"Why do we need weights in our recommendations?"
"How do weights make recommendations better?"
"Can you give examples of good and bad recommendation matches?"

You should understand:

✓ Why some matches should count more than others
✓ How weights improve logical recommendations
✓ When to apply different weights

Development Journey

Session 3: Database Setup

First, ask Claude about setting up PostgreSQL. Claude can guide you through:

Installing PostgreSQL
Adding pgvector
Testing the installation

Checkpoint: Can you:

✓ Connect to your database?
✓ Create a test table?
✓ Add the vector extension?

Session 4: Understanding Your Data

Ask Claude about:

Loading the Mountains vs Beaches dataset
Which fields are important for recommendations
How to structure your database tables

Checkpoint: Can you:

✓ Load the CSV data?
✓ Query the basic data?
✓ Understand the relationships between fields?

Session 5: Creating Embeddings

Work with Claude to:

Set up OpenAI API
Create your first embedding
Store embeddings in pgvector

Checkpoint: Can you:

✓ Generate an embedding from text?
✓ Store it in your database?
✓ Retrieve it correctly?

Session 6: Building Basic Recommendations

Ask Claude about:

Writing similarity queries
Testing with sample inputs
Improving match quality

Checkpoint: Can you:

✓ Find similar profiles?
✓ Get meaningful recommendations?
✓ Explain why matches are similar?

Session 7: Frontend Development

Ask Claude about:

Setting up the React form
Handling API calls
Displaying recommendations

Checkpoint: Can you:

✓ Get user input correctly?
✓ Show loading states?
✓ Display recommendations clearly?

Key Learning Checkpoints

After each session, ask yourself:

Could I explain this concept to someone else?
Do I understand why we're doing this step?
Can I predict what should happen next?

If "no" to any of these, ask Claude for clarification before moving on.

Working with Claude Effectively

Start Simple:

Ask for basic explanations first
Request real-world analogies
Build up to technical details

Show Your Understanding:

Explain concepts back to Claude
Ask if your understanding is correct
Request challenges to test your knowledge

When Stuck:

Explain what you're trying to do
Share what you've tried
Ask for step-by-step guidance

For Technical Issues:

Share error messages
Explain your setup
Ask for debugging strategies

Remember: The goal is understanding, not just copying code. Take time to grasp each concept fully before moving forward.

Session Time Management

Each session is 2 hours. Use this time to:

Review previous session's concepts (15 minutes)
Work through current session's material (80 minutes)
Test your understanding with checkpoints (15 minutes)
Prepare questions for next session (10 minutes)

If you finish early:

Review checkpoints again
Try explaining concepts to others
Experiment with what you've learned
Prepare questions for next session

If you need more time:

Note where you got stuck
Prepare specific questions for Claude
Review related concepts
Don't rush - understanding is key

Building a RAG-based Travel Recommender: Learning Journey

Session 1: Planning

Ask Claude:

"Can you explain the overall RAG system architecture?"
"What components will we need to build?"
"How will data flow through our system?"
"What development environment do we need?"
"Which APIs and tools should we prepare?"
"How should we structure our project?"
"What order should we build components in?"
"What are potential challenges we might face?"
"How can we test each component?"

You should understand:

✓ The complete system architecture
✓ Each component's role
✓ How components interact

Checkpoint: Can you:

✓ List all required tools and APIs?
✓ Explain where each tool fits in?
✓ Draw a system diagram?
✓ Create a development timeline?
✓ Identify critical dependencies?
✓ Plan your testing approach?

Session 2: Understanding Core Concepts

1. Understanding Vector Databases

Ask Claude:

"What is a vector database in simple terms?"
"How is it different from a regular database?"
"Can you explain vector similarity with a real-world example?"

You should understand:

✓ Why regular databases can't find "similar" items easily
✓ Why we need vector databases for recommendations
✓ What makes items "similar" in a vector database

2. Understanding Embeddings

Ask Claude:

"What are embeddings and why do we need them?"
"How do embeddings capture meaning?"
"Can you explain embeddings using a simple analogy?"

You should understand:

✓ How text gets converted to numbers
✓ Why similar meanings have similar numbers
✓ How embeddings help find similar items

3. Understanding Weights

Ask Claude:

"Why do we need weights in our recommendations?"
"How do weights make recommendations better?"
"Can you give examples of good and bad recommendation matches?"

You should understand:

✓ Why some matches should count more than others
✓ How weights improve logical recommendations
✓ When to apply different weights

Development Journey

Session 3: Database Setup

First, ask Claude about setting up PostgreSQL. Claude can guide you through:

Installing PostgreSQL
Adding pgvector
Testing the installation

Checkpoint: Can you:

✓ Connect to your database?
✓ Create a test table?
✓ Add the vector extension?

Session 4: Understanding Your Data

Ask Claude about:

Loading the Mountains vs Beaches dataset
Which fields are important for recommendations
How to structure your database tables

Checkpoint: Can you:

✓ Load the CSV data?
✓ Query the basic data?
✓ Understand the relationships between fields?

Session 5: Creating Embeddings

Work with Claude to:

Set up OpenAI API
Create your first embedding
Store embeddings in pgvector

Checkpoint: Can you:

✓ Generate an embedding from text?
✓ Store it in your database?
✓ Retrieve it correctly?

Session 6: Building Basic Recommendations

Ask Claude about:

Writing similarity queries
Testing with sample inputs
Improving match quality

Checkpoint: Can you:

✓ Find similar profiles?
✓ Get meaningful recommendations?
✓ Explain why matches are similar?

Session 7: Frontend Development

Ask Claude about:

Setting up the React form
Handling API calls
Displaying recommendations

Checkpoint: Can you:

✓ Get user input correctly?
✓ Show loading states?
✓ Display recommendations clearly?

Key Learning Checkpoints

After each session, ask yourself:

Could I explain this concept to someone else?
Do I understand why we're doing this step?
Can I predict what should happen next?

If "no" to any of these, ask Claude for clarification before moving on.

Working with Claude Effectively

Start Simple:

Ask for basic explanations first
Request real-world analogies
Build up to technical details

Show Your Understanding:

Explain concepts back to Claude
Ask if your understanding is correct
Request challenges to test your knowledge

When Stuck:

Explain what you're trying to do
Share what you've tried
Ask for step-by-step guidance

For Technical Issues:

Share error messages
Explain your setup
Ask for debugging strategies

Remember: The goal is understanding, not just copying code. Take time to grasp each concept fully before moving forward.

Session Time Management

Each session is 2 hours. Use this time to:

Review previous session's concepts (15 minutes)
Work through current session's material (80 minutes)
Test your understanding with checkpoints (15 minutes)
Prepare questions for next session (10 minutes)

If you finish early:

Review checkpoints again
Try explaining concepts to others
Experiment with what you've learned
Prepare questions for next session

If you need more time:

Note where you got stuck
Prepare specific questions for Claude
Review related concepts
Don't rush - understanding is key

Building a RAG-based Travel Recommender: Learning Journey

Building a RAG-based Travel Recommender: Learning Journey

Session 1: Planning

Session 2: Understanding Core Concepts

1. Understanding Vector Databases

2. Understanding Embeddings

3. Understanding Weights

Development Journey

Session 3: Database Setup

Session 4: Understanding Your Data

Session 5: Creating Embeddings

Session 6: Building Basic Recommendations

Session 7: Frontend Development

Key Learning Checkpoints

Working with Claude Effectively

Session Time Management

Related Skills

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Building a RAG-based Travel Recommender: Learning Journey

Session 1: Planning

Session 2: Understanding Core Concepts

1. Understanding Vector Databases

2. Understanding Embeddings

3. Understanding Weights

Development Journey

Session 3: Database Setup

Session 4: Understanding Your Data

Session 5: Creating Embeddings

Session 6: Building Basic Recommendations

Session 7: Frontend Development

Key Learning Checkpoints

Working with Claude Effectively

Session Time Management