> ## Documentation Index
> Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Section Intro
> Guide on using word embeddings for dynamic context in large language models to enhance chatbot workflows.
Welcome to your guide on leveraging word embeddings to provide **dynamic, relevant context** for large language models. In this lesson, you will:
* Understand what word embeddings are and why they’re crucial
* Learn to perform similarity searches on embedding vectors
* Augment prompts to [GPT-3.5 Turbo][GPT-3.5 Turbo] using retrieved context
By the end of this tutorial, you’ll be able to integrate custom datasets into your chatbot workflows, enhancing accuracy and relevance.
Ensure you have:
* A basic familiarity with Python
* An [OpenAI API key][OpenAI Embeddings]
* The `openai` Python package installed
***
## What Are Word Embeddings?
Word embeddings map text tokens into numeric vectors where semantic similarity is preserved. Models like [OpenAI Embeddings][OpenAI Embeddings] transform words, sentences, or documents into high-dimensional vectors.
* Similar tokens lie close together in vector space
* Enables efficient semantic search and clustering
## Why Use Embeddings for Contextual Retrieval?
When working with large language models, embedding-based retrieval lets you:
1. Maintain relevance—fetch only the most pertinent snippets
2. Scale gracefully—index millions of documents
3. Reduce prompt size—include concise context instead of entire texts
## Lesson Objectives
| Step | Description |
| -------------------------------- | ----------------------------------------------------------- |
| 1. Define Embeddings | Explain embedding concepts and dimensionality |
| 2. Perform Similarity Search | Compute cosine similarity to find nearest vectors |
| 3. Augment GPT-3.5 Turbo Prompts | Dynamically insert retrieved context into your API requests |
Ready to dive in? Let’s explore how to generate and query embeddings in Python.
***
## Links and References
* [OpenAI Embeddings][OpenAI Embeddings]
* [GPT-3.5 Turbo][GPT-3.5 Turbo]
* [Cosine Similarity Explained on Wikipedia](https://en.wikipedia.org/wiki/Cosine_similarity)
[OpenAI Embeddings]: https://platform.openai.com/docs/guides/embeddings
[GPT-3.5 Turbo]: https://platform.openai.com/docs/models/gpt-3-5-turbo