Getting Around the Token Limit in LLMs

If you’ve worked with large language models (LLMs) like GPT, Claude, or Mistral, you’ve probably hit the dreaded token limit at some point. It usually starts innocently—you feed in a long document or dataset, expecting a brilliant response. But partway through, the model just… stops. No warning, no goodbye. Just cutoff content.

What Is a Token Limit in LLMs?

Every LLM has a token limit—a maximum number of tokens (chunks of words or characters) it can handle in a single prompt + response. For example, GPT-4 Turbo currently supports up to 128,000 tokens, but most commonly used models hover around 4,000 to 16,000.

Hit that limit, and the model truncates either the input, output, or both—leading to incomplete results or broken workflows.

My First Encounter with the Token Ceiling

While working on a project involving large text datasets—think research papers and internal documentation—I ran into this problem fast. The model was doing great until the output started cutting off mid-sentence. It was frustrating, especially when you're depending on LLMs for summarization or analysis.

So I rolled up my sleeves and built a workaround: chunking.

Chunking: A Simple But Effective Strategy

Chunking means splitting your input into smaller, manageable pieces that fit within the token limit. Here’s how I made it work:

1. Split the Input: Break the long document into logical sections—paragraphs, chapters, or even sentences.
2. Process in Batches: Run each chunk through the LLM separately.
3. Merge Outputs: Combine the results into one coherent output.
4. Preserve Context: For better coherence, pass summaries of previous chunks or key points as part of the prompt for the next chunk.

It wasn't perfect—preserving continuity across chunks is tricky—but it worked well enough to get complete results without hitting the token wall.

Other Ways to Beat the Token Limit

While chunking is the go-to method, here are some other strategies that developers and researchers use:

1. Streaming Output

Some LLM APIs support streaming, where the model sends back output in real-time as it generates it. This can help with managing large outputs, especially if you're reading results on the fly and don't need the full context at once.

Bonus: Streaming makes your app feel faster and more responsive, even if it doesn’t technically increase the token limit.

2. Fine-Tuning Smaller Models

If you're working with repeated patterns or domain-specific text, fine-tuning a smaller model on your dataset can let you work within a smaller context window while still getting good results. It's especially useful for classification, extraction, or summarization tasks.

3. Using Context Compression

Tools like semantic compression, embedding-based summarization, or extractive summarization can reduce the input size before feeding it to the model—essentially saying "tell me what's important, first."

If you absolutely must process huge documents in one go, consider models with extended context limits (like Claude 2.1 or GPT-4 Turbo with 128K tokens). Just be prepared—they can be more expensive and slower. Final Thoughts

Token limits aren't going away any time soon. But that doesn't mean you're stuck. With chunking, streaming, and smart pre-processing, you can build powerful pipelines that get around these constraints.

It’s not always seamless, and sometimes it feels like hacking around limitations—but honestly? That’s half the fun.

If you're building with LLMs and running into similar issues, I’d love to hear how you’re tackling the token ceiling. Hit me up on Twitter or shoot me a message through my site.