> ## 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. # Dynamic Context > This lesson explores enriching large language models with up-to-date information using dynamic context from various datasets. In this lesson, we’ll explore how to enrich large language models (LLMs) with up-to-date information by injecting **dynamic context** from your own datasets—whether they’re confidential, private, internal, or public. This technique helps you build chatbots and applications that stay current beyond GPT-3.5’s September 2021 pre-training cutoff. ![The image illustrates the concept of "Adding Dynamic Context" with a diagram of a chatbot and categories labeled "Confidential," "Private," "Internal," and "Public."](https://kodekloud.com/kk-media/image/upload/v1752881579/notes-assets/images/Mastering-Generative-AI-with-OpenAI-Dynamic-Context/adding-dynamic-context-chatbot-diagram.jpg) ## Why Dynamic Context Matters Large language models are powerful, but their knowledge is frozen at the time of their last pre-training. To handle events, documents, or data generated after September 2021, you need a way to feed fresh information at query time. ![The image displays the text "Adding Dynamic Context" and "September 2021 (Cut-off date for the pre-training of GPT 3.5)" with a copyright notice from KodeKloud.](https://kodekloud.com/kk-media/image/upload/v1752881580/notes-assets/images/Mastering-Generative-AI-with-OpenAI-Dynamic-Context/adding-dynamic-context-september-2021.jpg) ## Core Workflow: Indexing & Retrieval Adding dynamic context involves two key phases: | Phase | Purpose | Example Tools | | --------- | ------------------------------------------ | ------------------------------------------ | | Indexing | Convert documents into vector embeddings | OpenAI Embeddings, Hugging Face Embeddings | | Retrieval | Find and return the most relevant passages | Pinecone, Weaviate, Elasticsearch | ### 1. Indexing * Break each document, FAQ, or dataset entry into chunks. * Generate a vector embedding for each chunk. * Store embeddings in a vector database (often called a “vector store”). ### 2. Retrieval * Compute the embedding for the user’s prompt. * Perform a similarity search against your vector store. * Retrieve the top-k most relevant passages. Adjust the value of k (top-k passages) based on token limits and response quality. ![The image is a flowchart illustrating the process of adding dynamic context, involving LLMs, similarity search, and output. Each step is represented by an icon and labeled accordingly.](https://kodekloud.com/kk-media/image/upload/v1752881580/notes-assets/images/Mastering-Generative-AI-with-OpenAI-Dynamic-Context/dynamic-context-flowchart-llms-similarity-output.jpg) ## End-to-End Sequence 1. **User Query:** A prompt is submitted through the chatbot UI. 2. **Embedding:** The application computes an embedding for the prompt. 3. **Search:** The vector store returns the most similar passages. 4. **Injection:** Retrieved passages are prepended (or appended) to the original prompt. 5. **LLM Call:** The augmented prompt is sent to the language model API. 6. **Generation:** The model uses both pre-trained knowledge and dynamic context to craft a precise answer. 7. **Response:** The chatbot displays the final output to the user. ![The image is a flowchart illustrating the process of adding dynamic context, involving a user interacting with a chatbot, which connects to search and retrieval, embeddings, and a large language model (LLM).](https://kodekloud.com/kk-media/image/upload/v1752881581/notes-assets/images/Mastering-Generative-AI-with-OpenAI-Dynamic-Context/dynamic-context-chatbot-flowchart.jpg) ## Next Steps: Hands-On Demo In the following section, we’ll implement a working prototype using the **AskUs** dataset, which contains events and information generated after GPT-3.5’s cutoff. You’ll see how dynamic context dramatically improves answer accuracy with real-world data. Ready to dive in? *** ## Links and References * [OpenAI Embeddings API](https://platform.openai.com/docs/guides/embeddings) * [Pinecone Vector Database](https://www.pinecone.io/) * [Weaviate Documentation](https://weaviate.io/developers/weaviate) * [GPT-3.5 Overview](https://platform.openai.com/docs/models/gpt-3-5)