As context windows expand in large language models, developers face new challenges in managing long prompts. The following techniques and tradeoffs are commonly used to deal with token limits in practice:
1. Hard Limits and Truncation
All models enforce a fixed maximum token limit. When input exceeds this limit:
- The oldest tokens (start of the prompt) are usually truncated first.
- This can happen silently or may trigger an error message.
For example, an open-source Claude multi-agent system showed logs like "Trimming prompt to meet context window limitations" when chats grew too large, confirming that it auto-dropped older content. Similarly:
- ChatGPT and Claude begin to forget early messages as the running total increases.
- This is because those messages are no longer part of the prompt – the model only sees what’s in the current window.
To work around this, developers often implement:
- Sliding windows – retaining only the most recent N turns of conversation.
- Summarization – compressing older messages into summaries.
Once truncated, information is irretrievably lost during generation – the model has no persistent memory beyond the current prompt.
2. Input/Output Balancing
Context windows combine input + output tokens into a single budget. You must reserve space for output when sending long prompts.
For instance:
- With a 128K model that allows 32K output, if your input is 128K, the model cannot respond with 32K tokens.
- You’ll receive a truncated completion or encounter an error.
In practice, developers set max_tokens for completion (e.g. 2,000) to ensure enough output space. Even if not used, the model reserves this space to avoid overflows.
OpenAI explicitly notes that input and output tokens are carried over in chat. As a conversation grows:
- All previous messages and responses eat into the token window.
- Even with hidden reasoning tokens, visible replies still accumulate.
This is why long chats eventually max out – every turn adds to the token count. To use long context effectively, developers must:
- Split the token budget carefully – e.g. send 90K of content and leave 10K for the model’s reply.
- Avoid sending the entire document if output space is needed.
Otherwise, the model may cut off mid-sentence or refuse to continue due to token constraints.
Understanding Quality Degradation and Token Overhead
3. Quality Degradation Near the Limit
All evidence points to a “graceful degradation” in output quality as you approach the max context size. Models become slower, and their responses often grow less precise or relevant. Studies and real-world trials have drawn a grim picture of advertised vs. effective context window - the closer you push toward the upper bound, the more likely the model will start forgetting or missing details.
For GPT-4-128K, one test found that beyond ~71K tokens (about 55% of the window), recall dropped off significantly - aligning with anecdotal reports that using more than half the window increases hallucination and error rates. Claude and Gemini show similar quality issues well before full capacity.
This doesn’t mean large contexts are useless - it means you should treat the upper bound as a buffer, not the default. If you have a 200K window, feeding in 50K of well-targeted info will perform better than stuffing 180K of loosely relevant data.
That’s why most developers working with long contexts still rely on Retrieval-Augmented Generation (RAG). Instead of supplying an entire knowledge base, they use vector search to fetch a handful of relevant chunks (typically a few thousand tokens) and supply those, even though the model could handle more. Why? Because curated prompts consistently yield better results.
The large context is best used as a “fit buffer” - making sure those chunks plus conversation history or reasoning tokens can coexist, not as a hard invitation to fill to the brim.
4. System Overhead and Token Reservation
Another nuance: system-level tokens often eat into your available context. OpenAI models, for instance, include hidden system messages and sometimes chat summaries. These count toward the token budget without being visible to users.
Google’s Gemini likely does the same - injecting formatting, moderation, or routing logic into the system prompt. Additionally, reasoning features consume tokens internally. OpenAI notes that the Assistants API can generate “a few hundred to tens of thousands” of reasoning tokens for complex queries. These tokens may be discarded before the final response, but they use up memory while the model is thinking.
This means the real user-available window is smaller than the advertised limit. And if your prompt triggers intensive chain-of-thought reasoning, the available input/output space dynamically shrinks.
As a result, developers must design with buffer space in mind. For example, in the 128K model, you might limit yourself to 100K input, 5K output, and leave ~23K for scratchpad computation. These tradeoffs aren’t obvious in marketing specs - but they’re critical for reliable application design.
5. “Lost-in-the-Middle” Effect (Memory Prioritization)
As described above, long-context LLMs exhibit a form of positional bias where they attend most to the beginning and end of the prompt, while middle content is often overlooked. This has been empirically observed for both GPT-4 (128K) and Claude 2 (100K+), and even formally documented in research studies.
The key insight: simply increasing the context size doesn’t mean all tokens are treated equally. Developers have learned to strategically order and chunk information to work around this limitation.
- Redundancy helps: If a fact is critical, include it at both the start and end of the prompt.
- Prompt ordering matters: Place questions or key tasks after the reference material (near the end of the prompt) to benefit from recency bias.
- Dialogue expectations: Models prioritize the latest user message, assuming it's the main query. Earlier system context is deprioritized.
In short, position matters. LLM context is not a simple FIFO queue - not all tokens live equally in the model’s “attention”.
6. Automatic Summarization & System Messages
To cope with limited context space, some platforms employ automatic summarization. For example, long chats may be compressed into a summary and inserted as a system message to preserve state.
OpenAI’s ChatGPT likely does this behind the scenes (though undocumented), while open-source tools like OpenHands explicitly use this method.
The tradeoff: older content is no longer verbatim. If a detail is omitted from the summary, it's effectively forgotten. In fact, one developer noted a bug where their summarizer (called a “condenser”) dropped recent messages and caused the AI to forget its own progress.
Even when working correctly, this mechanism can cause the model to reference earlier parts of the chat vaguely (“As we discussed…”) without recalling specifics - because it only sees the summary.
Best practices:
- Carefully construct summaries to preserve essential instructions and facts.
- Use a dedicated summary message in every prompt turn (usually a
systemorinitial userrole message). - Monitor hallucinations or breakdowns as a sign the model lost important raw context.
Summarization can extend conversation length in appearance, but only if summaries are accurate. Otherwise, critical knowledge may silently vanish.
Conclusion: Use Long Contexts Wisely
Today’s ultra-large context windows represent a genuine leap in AI capability. Models like GPT-4 (128K), Claude (100K-200K), and Gemini (1M+) now enable interactions with lengths that were unimaginable just a few years ago.
However, these advertised context sizes are theoretical upper bounds. In practice, effective usage falls short due to various system-level behaviors:
- Hard truncation or errors when limits are exceeded
- Memory prioritization: recent and initial tokens receive more attention
- Internal token usage: models discard or compress content to create space
- Quality degradation as you approach the limit
More tokens is not always better. Developers must prioritize important information in prompts and assume the model won’t treat every token equally - especially beyond 50-70% utilization.
In real-world use:
- Trim or remove irrelevant content
- Summarize older dialogue when possible
- Use retrieval-based approaches to show only the most relevant snippets
- Design for degradation: expect models to miss facts in dense or middle sections
Think of the context window as a bandwidth-limited buffer, not infinite memory. While you could technically dump an entire novel into the model, it may only retain the CliffsNotes version internally.
Mastering prompt design and understanding
Sources
This analysis is based on real-world developer reports and official documentation, including:
- OpenAI community discussions and GPT-4 recall tests
- Anthropic Claude usage experiments and guides
- Google AI Studio and Gemini user forum reports
These accounts reinforce that context window size is not equivalent to usable memory, and effective prompting strategies are essential for taking full advantage of modern LLMs.