LLM Embeddings & RAG Basics

Training materials for learning to work with Large Language Models, embeddings, and Retrieval Augmented Generation (RAG) patterns in research workflows.

What is this?

A hands-on training repository that teaches:

• How to make API calls to LLMs from Python
• What embeddings are and how to use them
• How to build RAG (Retrieval Augmented Generation) systems
• Practical applications: qualitative coding and knowledge chatbots

Who is this for?

Research staff who want to:

• Use LLMs programmatically (not just through chat interfaces)
• Work with their own data and documents
• Build AI-augmented research tools
• Understand embeddings and semantic search

No prior LLM experience required. Basic Python knowledge helpful.

Repository structure

llm-embeddings-rag-basics/
├── docs/                    # Session guides with step-by-step instructions
│   ├── session_01.md       # Local setup & first API call
│   ├── session_02.md       # Introduction to embeddings & RAG
│   ├── session_03.md       # Qualitative coding with embeddings
│   └── session_04.md       # Building a knowledge chatbot
├── examples/                # Runnable Python scripts
│   ├── 01_test_connection.py
│   ├── 02_translate_text.py
│   ├── 03_embeddings_intro.py
│   ├── 04_semantic_search.py
│   ├── 05_qualitative_coding.py
│   └── 06_knowledge_chatbot.py
├── src/                     # Reusable code modules
│   ├── client/             # LLM client setup
│   ├── embeddings/         # Embedding utilities
│   └── rag/                # RAG pipeline components
└── data/                    # Sample data for exercises
    ├── sample_interviews/
    └── project_lessons/

Training sessions

Morning

1. Session 01 (1 hour): Local setup and your first LLM API call
2. Session 02 (1 hour): Introduction to embeddings and RAG models

Afternoon

3. Session 03 (1.5 hours): Qualitative coding with embeddings
4. Session 04 (1.5 hours): Building an internal knowledge chatbot

Getting started

1. Clone this repository
2. Follow the setup instructions in docs/session_01.md
3. Complete sessions in order

Each session builds on the previous one.

Requirements

• Python 3.11+
• OpenAI API key
• Code editor (VS Code or Positron recommended)
• Basic command line familiarity

Support

• Session guides contain detailed instructions and troubleshooting
• Example scripts include inline comments
• Common issues documented in each session guide

Learning outcomes

By the end of this training, you will:

• Have a working local LLM development environment
• Understand how to use LLM APIs programmatically
• Know what embeddings are and when to use them
• Understand the RAG architecture pattern
• Have built two practical applications using these concepts

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Start here: docs/session_02_embeddings_rag.md