With the arrival of GPT-5 and its context windows expanded to millions of tokens, a new era is opening up for web applications and artificial intelligence use cases. Being able to load thousands of pages of documentation at once, the complete history of a customer relationship, or an entire software project would have seemed unthinkable until recently.

Naturally, the temptation is strong for developers and organizations to put everything into the model: raw text, knowledge bases, logs, user data, an approach now known as Context Cramming.

But more context does not automatically mean more intelligence. In fact, this reflex can harm accuracy, performance, and even the security of AI systems. Understanding the limits of Context Cramming is the first step toward a more strategic and sustainable use of these new tools.

What is Context Cramming?

The term Context Cramming comes from the English verb to cram, meaning to stuff or pack, like a student cramming the night before an exam, trying to absorb as much information as possible without real organization.

Applied to language models, the concept refers to massively filling an LLM’s context window with everything available, without relevance filtering or prioritization. This may include:

• entire documents copied and pasted,
  
• full conversation histories,
  
• databases exported in bulk,
  
• or unfiltered technical instructions.

At first glance, this approach seems logical: the more data the model sees, the “smarter” it should be. In practice, however, the opposite happens. Too much raw information creates noise, dilution, and often a drop in the quality of responses.

Context Cramming is therefore a form of naïve optimism toward expanded context windows; a belief that more equals better, when real value lies in selecting and structuring the injected information.

Why is this a problem?

While giving the model as much information as possible may seem appealing, Context Cramming actually leads to several negative effects. From information dilution to rising costs and security risks, this practice raises issues that are essential to understand.

1. Dilution of key information
When a model is flooded with data, truly relevant elements get lost in the mass. As a result, the LLM struggles to prioritize what matters and produces vague, approximate, or off-target responses.

2. The amnesia effect
Even with expanded context windows, models do not assign equal weight to every token. Information placed far earlier in the context tends to lose influence, as if it gradually fades. Cramming can therefore make critical data effectively invisible to the model.

3. Performance and cost
The larger the context, the slower the inference and the higher the cost. Injecting thousands of unnecessary tokens wastes resources without delivering tangible improvements in response quality.

4. Security risks
Stuffing the context with full exports (logs, customer data, internal documents) mechanically increases the risk of accidental leaks. A poorly phrased query can expose sensitive information that should never have been included in the first place.

In short, Context Cramming turns an opportunity, expanded context windows, into a liability. It is not just a poor technical practice, but a strategic barrier to intelligent AI adoption.

Concrete examples of Context Cramming

Context Cramming may seem abstract, but it already shows up in everyday interactions with ChatGPT and other AI assistants.

In everyday use of ChatGPT

• The massive prompt: You copy and paste several pages of text at once (for example, a long document or an entire course) and ask the model for a summary. The result is often vague or overly general, because key points are diluted in the mass.
  
• The endless conversation: After dozens of exchanges, ChatGPT seems to forget details from the beginning. You have to repeat instructions or rephrase, an indication that the model cannot efficiently process the entire history.
  
• The overly broad request: “Here is my entire 20-page business plan, write me a marketing strategy.” The response is often generic, because the model has not filtered what was truly relevant in the information flood.

These situations are already forms of Context Cramming: too much information, poorly organized, leading to lower-quality AI output.

In AI development and integration

The same phenomenon occurs when building more technical applications:

• A customer support chatbot that receives the full history of tickets and conversations instead of a targeted summary → the AI responds slowly, confusingly, and sometimes misses the actual request.
  
• A documentation assistant that loads an entire 500-page technical manual to answer a single question → the response becomes generic, whereas an intelligent retrieval system could have targeted the correct section.
  
• A development tool where the entire codebase is injected into the context to fix a bug → the request is costly, and the model is likely to get lost instead of pinpointing the specific issue.

In short, whether as an end user or a developer, Context Cramming leads to the same outcomes: confusion, slowness, overly vague answers, and wasted resources.

The alternative: context discipline

In response to the limits of Context Cramming, another approach emerges: context discipline. The idea is no longer to put everything into the model, but to intelligently select what is truly needed for each interaction.

This discipline relies on several complementary practices:

• Context curation > Pruning > Distillation : filtering, trimming, and condensing information to keep only what matters.
  
• RAG (Retrieval-Augmented Generation): using a vector search engine to inject only the documents relevant to the question being asked.
  
• Adaptive summarization: transforming overly long text blocks into lighter syntheses before passing them to the model.
  
• Prompt structuring: organizing information with headings, sections, and tags to help the AI better understand and prioritize what it receives.

At Beet, we already integrate these practices into the development of our web applications. Rather than giving in to the ease of “putting everything in,” we design systems that optimize the use of expanded context windows.

Why guidance makes all the difference

In other words, expanding context windows is a tremendous opportunity, but only a disciplined approach allows real value to be extracted. This is exactly what we implement at Beet: turning a technical constraint into an efficiency lever for our clients.

The increase in context windows with models like GPT-5 represents a major advancement in AI. But as is often the case in technology, raw power means nothing without strategy. Context Cramming perfectly illustrates this trap: believing that piling on data will lead to better results, when in reality the opposite often happens.

Beyond pure technical development, this requires thoughtful design and strategy. Organizations need guidance to implement the right practices: filtering, summarizing, structuring, prioritizing. In short, transforming information abundance into truly usable intelligence.

At Beet, we believe that the value of AI lies not only in what it can do, but in how it is used. Our role is to help organizations avoid the deceptive simplicity of “putting everything in” and adopt solid, secure, high-performing approaches.

Because in the end, it is this discipline that makes the difference between an AI that impresses… and an AI that truly transforms organizations.