RAG Context Engineering
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Introduction to Context Engineering
Imagine you're working with a large language model (LLM) and you need to get a specific answer from it. You've probably noticed that the quality of the answer depends on the context you provide. Context engineering for RAG is a practice that helps you improve the accuracy of your LLM by providing the right context. This practice was named by Tobi Lütke and Andrej Karpathy in 2025.
What is Context Engineering?
Context engineering is a technique used to provide the necessary context to a large language model. It involves four typed inputs: corpus, conversation, tool extensions, and follow-up work. These inputs help the model understand the context of the question and provide a more accurate answer.
The Four Typed Inputs
- Corpus: This is the main body of text that the model is trained on.
- Conversation: This refers to the conversation history between the user and the model.
- Tool extensions: These are additional tools or features that can be used to improve the model's performance.
- Follow-up work: This includes any additional work or processing that needs to be done after the initial answer is provided.
Why Context Engineering Matters
Context engineering is important because it helps to improve the accuracy of LLMs. By providing the right context, you can get more accurate answers from the model. This is especially important in applications where accuracy is critical, such as in enterprise document intelligence.
How to Use Context Engineering
To use context engineering, you'll need to identify the four typed inputs and provide them to the model. Here are the steps to follow:
- Determine the corpus: Identify the main body of text that the model will be trained on.
- Establish the conversation: Set up a conversation history between the user and the model.
- Add tool extensions: Identify any additional tools or features that can be used to improve the model's performance.
- Plan follow-up work: Determine what additional work or processing needs to be done after the initial answer is provided.
Tools for Context Engineering
There are several tools available that can help with context engineering. Check the official sites of these tools for current pricing and features. Some popular tools include language models like LLaMA and PaLM.
The Verdict
Context engineering is a crucial practice for anyone working with large language models. By providing the right context, you can improve the accuracy of your model and get more accurate answers. Whether you're working in enterprise document intelligence or another field, context engineering is a technique that's worth learning about.
