Context is the “memory” of a large language model. It determines what the model sees, how it interprets the query, and what data it considers when forming the answer. In recent years, the volume of this context has grown hundreds of times, from models previously remembering only a few paragraphs to GPT-5’s ability to process up to 400,000 tokens, and Google’s Gemini 1.5 Pro’s ability to process up to 1 million tokens.
What does this mean for the user? A modern model can “hold” an entire book, codebase, or long document in its head. This allows it to work with large amounts of information without losing essential details halfway through.
This article explores the components of context, its structure, limitations, and how it interacts with the most common use cases, such as using tools (calculator, MCPs) and incorporating additional knowledge through augmented retrieval generation (RAG).
The main components of context
When we talk about the “context” of a large language model, we mean everything it receives as input before generating an answer.
- System instructions
These are hidden rules that govern the behavior of the model. They determine its “personality” and style: how to respond, what can and cannot be done, and what tone to keep. The user does not see them, but they are what make the same model behave differently in different services.
- User request (prompt)
This is the central part of the context, which the user enters. It can be very short (“explain what quantum physics is in simple words”) or long and structured, like a technical task or document.
- Dialogue history
In many cases, the model does not work “from scratch.” It remembers previous messages within the context window, creating the effect of a conversation when you can clarify and return to previous topics. But there is a limit: If the story is too long and goes beyond the size of the context, the older parts “fall out.”
- Additional data (RAG, tools).
When there is not enough context, additional mechanisms are used. Retrieval-Augmented Generation (RAG) allows you to load knowledge from a database or search, and calling tools (for example, a calculator or API via the Model Context Protocol) will enable the model to obtain information outside its own “knowledge”. All this is also built into the context to make the answer as accurate as possible.
Context limitations
Even though modern models can already “remember” hundreds of thousands or even millions of tokens, context remains a limited resource. And this has several significant consequences:
- Displacement of old information
The context window works like a sliding memory. If a conversation or document becomes too long, the oldest parts fall out of the model’s field of view. Even with a million tokens, the model will “forget” the beginning if you exceed this limit.
- Uneven attention to data
Research shows that the model does not pay equal attention to each piece of text. Information closer to the end of the context often has a more substantial impact than data from the beginning.
- Increasing computational costs
The larger the context, the more expensive and slower the model runs. Analyzing a million tokens requires more memory and computing resources than short queries.
- The risk of “noise”
When too much information is loaded into the context, the necessary facts can get lost among the unnecessary ones. This leads to the model either not finding what it needs or giving a less accurate answer.
Context in tool use
When the model calls external tools (for example, a calculator, database, or API via the MCP protocol), this entire process is also “stitched” into the context. That is:
- The user query becomes part of the context.
- The model generates instructions for the tool, which are also stored.
- The tool’s response is returned to the context so the model can use it for the final answer.
The chain is: request – call – result – integration into the context. If there are many tools, the context grows quickly, and then you have to carefully control which data to leave so that the model does not drown in “noise”.
Context in Retrieval-Augmented Generation (RAG)
RAG works like this: when a user asks a question, the system searches for relevant documents in an external database and loads them into the context. The model has no “built-in” knowledge about these documents, but, having received them in context, it can give the correct answer.
The quality of RAG is actually determined not so much by the power of the model as by how well the search and selection of data for the context are organized.
When using the tools, the language model becomes a control center that coordinates data from different sources. If the intermediate results are organized chaotically, the model can lose cause-and-effect relationships and make decisions based on partial or distorted information.
In RAG, if the quality of the context is strong and clean, the model can generate answers that look like well-founded conclusions of an expert. But if duplicates, incompatible passages, or unverified sources remain in the context, the risk of “hallucinations” increases when the system creates convincing but incorrect text. That is why modern solutions for RAG increasingly include multi-level filters and data checks before they enter the context. Companies like Keymakr, which specialize in data annotation, validation, and creation, play a crucial role here by ensuring that the underlying datasets are accurate, consistent, and structured for reliable AI performance. Ultimately, user trust and the practical value of the entire system depend on this.
Context menu in OpenAI
In May 2025, OpenAI introduced the Responses API, which opened a new approach to working with context. Now, a model can call tools directly within a single dialog, from web search to Code Interpreter and file search. Thanks to the MCP (Model Context Protocol), all these calls are integrated into a single “context stream” where the logic and sequence of interactions are stored.
When a model receives a result from an external service, it returns to the context for the following response. This allows you to create complex multi-step decision chains, which makes OpenAI agents more accurate and flexible.
How to optimize context
Since context is limited and expensive to use, the main task of developers and product designers is to fit only the most valuable information into it. There are several practices for this:
- Prompt engineering
A correctly formulated query saves hundreds of tokens. For example, you can give clear instructions instead of long explanations: “Summarize in three points…” instead of “Please give a brief overview of no more than a few sentences…”.
- Chunking in RAG
Documents are rarely added to the context entirely. They are broken into small “chunks,” and only those most relevant to the query are served.
- Compression of dialogue history
When a conversation is long, it can be summarized periodically; instead of storing hundreds of messages, a summary of key points can be created.
- Re-ranking of documents
If RAG returns many potentially relevant fragments, serving them all is unnecessary. You can apply an additional model or algorithm to re-evaluate (re-rank) and select only the most valuable parts.
- Summarizing big data
Sometimes, you need to work with massive sets. Instead of downloading everything, you compress the sets in the form of summaries or excerpts and then add them to the context.
Context is a strength, but also a weakness of LLM
One of the most well-known risks is prompt injection. This technique manipulates how an AI model interprets instructions by inserting unexpected inputs into a prompt to reveal unauthorized information.
Another risk is sensitive data leakage. When a user adds confidential materials, reports, code, or medical data to a context, it becomes part of the model’s working memory. If the system is not configured correctly, this data can be “leaked” in responses to other users or remain in logs.
Long documents also pose a risk of manipulation through noise. If a large document with redundant information is loaded into a context, the model may pay more attention to secondary or false statements.
The future of context: the evolution of LLM
AI leaders are significantly expanding the capabilities of their models, increasing the size of the context window to hundreds of thousands and even millions of tokens.
By 2027, large language models are expected to efficiently process contexts of millions of tokens, thanks to architectural innovations such as state-space models and Mixture-of-Experts (MoE). This will allow the model to “memorize” large amounts of information without losing efficiency.
The true power of modern LLMs is manifested when context is used thoughtfully. The ability to properly organize information, select relevant fragments, and control its quality allows the model to work as efficiently as possible, giving users accurate, safe, and valuable results.