What AI Models Actually Do With Context

A quick refresh on the mechanics and limits of context windows in LLMs

How does ChatGPT actually interpret your questions? Large language models rely heavily on context, and the quality of that context directly shapes the quality of the response. As I’ve been digging into ways to be smarter about context, I thought it would be useful to break down how it’s actually processed at the token and attention level. By looking at how words become tokens the model can work with and where the limits of context appear, we can get a clearer picture of both the strengths and constraints of these systems, and why context and memory have become such important design spaces.

Even if you don’t care about context engineering, ChatGPT has 800 million weekly active users, so if you’re reading this you’re probably one of them and it’s worth taking a moment to appreciate how it works.

Context defined in LLM terms

When we talk about context in large language models, we’re referring to the full sequence of tokens the model processes in a single forward pass. That includes the conversation history so far, any retrieved documents added through a retrieval system, and the instructions or prompts that frame the interaction. In other words, context is everything you place into the prompt. It’s distinct from the model’s underlying knowledge, which is encoded in its parameters; context is more like working memory for a single exchange.

How LLMs “ingest” tokens

Before a model can process language, the text has to be broken down into units it can handle. This starts with tokenization, where input text is split into subword pieces. For example, the sentence “Large models process text” might tokenize as

[Large] [models] [process] [text]

More complex words get split further, so “contextualizing” might become

[context] [ual] [izing]

Each of these tokens is then mapped to a high-dimensional vector, the embedding. The embedding carries learned semantic and syntactic information, placing related words closer together in vector space. This gives the model a numerical landscape where “cat” and “dog” are nearer to each other than to “car,” making it possible to capture meaning and relationships through geometry rather than symbols alone.

LLMs are built on the transformer architecture, which processes all tokens in a sequence at once rather than one by one, so they don’t naturally know the order of the sequence. To address that, each token embedding is combined with a positional encoding, which is an additional signal that marks its place. This way, the model can tell the difference between “the cat chased the dog” and “the dog chased the cat,” even though the same words are present.

It’s worth noting that this process matters not only at inference, when you prompt the model, but also during training. Models are trained with the objective of predicting the next token given all previous tokens in the sequence. In other words, learning itself is entirely context-driven, and every update to the weights depends on how well the model uses context to guess what comes next.

Once embeddings and positions are in place, we enter the key next stage: the attention mechanism, which integrates information across the sequence.. Inside each layer of the model, the input vectors are transformed into three different representations: queries (Q), keys (K), and values (V). You can think of queries as “what a token is looking for,” keys as “what a token has to offer,” and values as the actual content carried along. By taking the dot product of Q and K, the model scores how relevant one token is to another. Those scores are normalized to create attention weights. These weights decide how much each token should “pay attention” to the others, and the values (V) are mixed together accordingly.

In practice, transformers don’t rely on a single set of Q/K/V projections. They use multi-head attention, where multiple attention “heads” run in parallel. Each head can specialize in capturing different relationships - one might track word order, another might capture long-range dependencies, and another might focus on entity co-reference. The outputs are then combined, giving the model a richer view of the sequence.

By stacking many transformer layers, each token’s representation gets progressively enriched with information from the others. Even in the first layer, a token can already attend to nearby words, but deeper layers allow the model to capture longer-range and more abstract dependencies. Take the sentence “The scientist who won the award gave a lecture.” In the early layers, “scientist” may mostly connect to its immediate neighbors like “who” or “won.” After several layers, its representation also incorporates signals from “gave a lecture,” allowing the model to resolve that it was the scientist (and not the award) who delivered the talk. This progressive refinement is what makes stacking layers so powerful: each pass through the network adds another layer of understanding, and at scale, this is a big part of why larger models demonstrate such impressive reasoning and fluency.

Why more context isn’t always better

In a perfect world, we could feed a model unlimited information once and expect consistently accurate responses forever. Obviously, that’s not the case, so it’s worth looking at the main limitations.

One issue is attention dilution. Recall that attention works by computing similarity scores between every pair of tokens in the sequence. When the sequence is short, it’s easy for the model to focus sharply on the most relevant tokens. But as sequences get very long, each token is competing with thousands of others. The important signals don’t disappear, but their weights get spread thin across the noise, so the model ends up attending less strongly to what actually matters.

A related empirical finding is the “lost in the middle” problem. Studies show that models tend to allocate more attention to the start and end of a sequence, with the middle receiving disproportionately less focus. This bias means that dropping a key fact halfway through a long prompt is riskier than putting it at the top or bottom, and the model is more likely to miss it.

Another limitation is prompt injection and biasing through excess context. With larger windows, the model has to weigh far more tokens, and the attention mechanism doesn’t inherently distinguish between useful and misleading information. This makes it easier for irrelevant or adversarial content to steer the output. Sometimes this happens accidentally - for example, when a tangential detail in the middle of a long document ends up outweighing a more important fact. It can also be deliberate, as in prompt injection attacks where hidden instructions are slipped into retrieved text. The bigger the context, the greater the chance that irrelevant or malicious input gets treated as if it were authoritative.

There’s also the issue of efficiency. Attention requires each token to compare itself with every other token, so the compute and memory load grows quadratically (O(n²)). Even if you don’t care about latency, that complexity still impacts quality: models can’t be trained on arbitrarily long contexts because the cost would be infeasible. As a result, they’re only optimized up to a fixed maximum length (e.g., 4k, 32k, or 1M tokens). Pushing beyond that window often leads to degraded performance, since the model was never trained to handle those lengths. In practice, this means efficiency bottlenecks during training translate directly into limits on how much context a model can reliably use at inference.

Another subtle limitation is distribution shift. Models are trained on sequences up to a certain length, often sampled randomly. Feeding them contexts much longer than what they saw during training means you’re pushing them out of distribution. Even if the architecture allows it, performance can degrade simply because the model was never optimized to handle that kind of input.

Strategies to maximize usefulness

Given these limitations, the challenge on the application side is about using context windows wisely. Techniques like prompt reordering, semantic chunking, compression, or retrieval-augmented generation all help make context windows more efficient. Context engineering is essentially finding ways of working around the fact that LLMs only have short-term memory.

It’s helpful to distinguish between three layers of memory in these systems. First, parametric knowledge, which lives in the model’s weights and reflects what it absorbed during pretraining. Second, short-term memory, which is the active context window, the tokens in play for a single request. And third, long-term memory, which resides outside the model in external systems like vector databases or knowledge graphs. Many failures people describe as “bad memory” are really boundary issues between these layers: either trying to cram long-term knowledge into a short-term window, or expecting parametric knowledge to update dynamically when it can’t.

To actually extend memory, you need that third layer of long-term stores that persist beyond a single request. These can take the form of vector databases where chunks of text are stored as embeddings, structured knowledge graphs that encode relationships, or more specialized systems that enforce hierarchy and selectivity. These external stores act as the model’s external working history, complementing the static knowledge in its weights.

The interesting challenge is bridging short-term and long-term memory. Agents and retrieval systems have to decide what to surface from long-term stores and bring into the short-term window at the right moment. Sometimes that’s as simple as pulling semantically similar passages from a vector DB. Other times it’s more structured, like summarizing past conversations, pruning branches of a decision tree, or dynamically rewriting the context to reflect only what’s relevant. The research frontier is full of variations here: hybrid retrieval pipelines, memory-augmented agents, and learned retrieval policies that decide what belongs in context without human hand-crafting.

Looking ahead

There’s been a major evolution of context windows in just the past few years. Today, GPT‑5 supports upwards of 256K tokens, with API access reaching 400K tokens per invocation. At the same time, Gemini 1.5 Pro and Anthropic’s Claude are pushing into the multi-million‑token range - Gemini at up to 2 million, Claude at 1 million, and experiments even reaching 10 million.

Yet pushing tokens isn’t the entire story. Cutting-edge research is uncovering smarter, more efficient ways to extend context without paying the usual compute and memory penalties. Techniques like LongRoPE2, YaRN, and positional interpolation stretch context lengths by modifying positional encodings. Meanwhile, innovations in attention such as linear attention, Core Context Aware (CCA) attention, and memory-augmented architectures, are starting to break free from quadratic scaling bottlenecks.

Ultimately, it’s an open question which path will prove more valuable: sheer length or smarter deployment of context. Huge windows let us feed full books, entire codebases, or headed-for-forgetting conversation threads into the model, but studies show models still misweight or ignore much of that content. Smarter context via pruning, compression, or memory systems may well be the key to reliable reasoning and long-term coherence. Or perhaps even more revolutionary, new architectures may emerge where attention is neither quadratic nor constrained by token-only memory.

In that sense, context is both a bottleneck and a design frontier. Watching how models scale, retrieve, remember, and reason in this expanding space is one of the most exciting developments in AI right now.