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Documentation Index

Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt

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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."

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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.

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Core Workflow: Indexing & Retrieval

Adding dynamic context involves two key phases:
PhasePurposeExample Tools
IndexingConvert documents into vector embeddingsOpenAI Embeddings, Hugging Face Embeddings
RetrievalFind and return the most relevant passagesPinecone, Weaviate, Elasticsearch

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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”).

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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.

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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).

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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?

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