5 Approaches to Solve LLM Token Limits

• LLM token limits remain a practical bottleneck in 2025. Even though context windows have grown (Gemini 1.5 Pro with 1M tokens), everyday workflows still hit the ceiling when working with significant document inputs.
• Chunking, summarization, and fine-tuning remain the primary strategies, but developers now combine them with semantic search and RAG pipelines.
• New ways to solve token limits include parallel processing and streaming. In 2025, workflows often do not rely solely on one model to handle everything. Instead, they spread jobs across multiple models and combine the results, which makes handling long contexts more scalable.

Large language models (LLMs) like ChatGPT and Claude 3.5 are at the core of 2025’s AI workflows, but they still face limitations, especially related to LLM token size.

Training an LLM requires a large text corpus, a robust neural network architecture, and a tokenizer module to break down the text. The tokenized text is passed to a neural network with a self-attention mechanism, which helps it focus on key aspects of the text. The text tokens are stored in memory before the neurons in the network process them. The larger number of tokens, the more memory is required, so LLMs limit the number of tokens they can process.

The token limit places certain boundaries on the applications of the language model. This article will discuss a few methods to solve the tokens limit problem. But first, let’s understand what a token is.

Tokens may include words, parts of words, punctuation, or whitespace, depending on the tokenizer used. Each LLM employs a specific tokenization algorithm to segment the input text into discrete parts.

Token management has always been an essential part of NLP. Still, it’s even more critical now, in 2025, because context windows are expanding rapidly (for example, Gemini 1.5 Pro has 1 million tokens and Claude 3.5 Sonnet has 200,000). However, it also means that tokens must be handled efficiently to remain scalable and keep costs low.

• 1 token ~= 4 chars in English
• 1 token ~= ¾ words
• 100 tokens ~= 75 words

Or

• 1–2 sentences ~= 30 tokens
• 1 paragraph ~= 100 tokens
• 1,500 words ~= 2048 tokens

For example, using the OpenAI Tokenizer, the sentence “This is just some random text that ChatGPT will break into tokens…” is split into 36 tokens, as shown below:

OpenAI’s Tokenization Model

Each tokenization method has advantages and drawbacks and is used according to the model and application requirements. Common tokenization techniques include:

Word-based Tokenization 

A text document is broken down into words to form the required tokens. This text splitting is also called rule-based tokenization since the process involves certain hard-coded rules to identify an individual word. The most common rule is splitting based on white space. Let’s take the following sentence as an example: “It’s a sunny day!” Splitting the sentence based on white space would yield us the following tokens;

“It’s”, “a”, “sunny”, “day!”. 

These words provide more information to the machine-learning model as they can be processed individually. However, in modern LLMs, tokenization goes beyond whitespace splitting, as contractions and punctuation are often separated for precision, as shown in the visual example above.

Splitting Further

The sentence we used in the previous example contains contractions and punctuation, and it is clear that plain white-space splitting is not the best choice. We can apply additional rules to tokenize complex sentence structures in this case. This way, we will split “day!” into “day” and “!”. The exclamation mark hints at the model of emphasis in this sentence. Furthermore, the contraction “it’s” is split into “it” and “s” to build a sense of the full term “it is.”

Keras Tokenizer

The Keras tokenization model can be accessed via the keras.preprocessing.text class. The tokenizer module creates a dictionary of vocabulary based on the text passed to it. The module preprocesses the text to all lower-case by default. Here’s a Python example of how you can use the Keras tokenizer.

from keras.preprocessing.text import Tokenizer

documents = ['A Lot of random text that is to be tokenized by the Keras tokenizer',

            'The Keras Tokenizer takes in multiple text documents to fit its Tokenizer',

            "It's a convenient way to preprocess text for NLP training"]

tk = Tokenizer(num_words=100)

tk.fit_on_texts(documents)

print(tk.word_index)

Output:

{'text': 1, 'to': 2, 'tokenizer': 3, 'the': 4, 'keras': 5, 'alot': 6, 'of': 7, 'random': 8, 'that': 9, 'is': 10, 'be': 11, 'tokenized': 12, 'by': 13, 'takes': 14, 'in': 15, 'multiple': 16, 'documents': 17, 'fit': 18, 'its': 19, "it's": 20, 'a': 21, 'convenient': 22, 'way': 23, 'preprocess': 24, 'for': 25, 'nlp': 26, 'training': 27}

The tokenizer indexes each element of the corpus. These elements can be further used to generate vector representations of the text.

An LLM model size is defined by the number of input tokens it can accept. Each model has a different limitation. Since the tokens will be stored and processed in memory, the limitations help keep the model efficient and optimize resource utilization. As of 2025, here are the latest LLM token size limits for top models:

Despite these increases, token limits still affect summarization, legal analysis, and code processing.

Although the max token limitation is necessary, it defines the LLM parameters and limits the model’s performance and usability. Having an upper bound of the token count means the model cannot process any text beyond it. Any contextual information outside the max token window is not considered during processing and may limit the results. It also hinders users with large text documents for processing. Let’s discuss a few ways to solve the token limitation problem.

Certain techniques can help in overcoming token limitations. Let’s discuss these in detail.

Truncation

The easiest way to bring your text within the max token limit is to clip it from either end. Clipping means we remove words/sentences from the text’s start or end. It is a simple fix, but it comes at the cost of loss of information. The model will not process the truncated text and might miss the important context.

Truncation can be done on a character or word level, depending on the requirement. The following Python code shows how to truncate the text from the end of the sentence.

def truncate(long_text: str, i: int):
   return ‘ ’.join(long_text.split()[:-i])
txt = "This is probably a long sentence!"
short_text = truncate(txt, 3)
print(short_text)

Chunk Processing

Another method of processing long text bodies is by breaking the text into smaller chunks. Different chunking strategies exist to split texts. Firstly, the strategy that comes to mind is to split on a consistent fixed chunk size according to raw token counts. Chunks can be created on a sentence level by splitting text while respecting the boundaries of sentences. This sentence-splitting technique can be further enhanced by introducing sliding windows with buffering surrounding sentences (thereby adding surrounding context) of the split. Also, chunking can be done with semantics as a focus, such that instead of chunking text with a fixed chunk size, the semantic splitter adaptively picks the breakpoint between sentences using embedding similarity with this concept proposed by Greg Kamradt. Lastly, chunking can be performed on a relation basis such that chunks have several hierarchies of different sizes referencing their parent node.

Chunking processing overview

Each chunk is passed individually as input to the LLM and produces independent results. The results are then combined to form a single output. However, the final result is prone to errors since the individual chunks contain only part of the overall information, and stitching the final results may still leave gaps. These chunks can then be converted into numeric representations (embedding or vectors), encapsulating text semantics and enabling efficient LLM processing. These embeddings can be readily stored in a vector store or database where relevant chunks can be retrieved using similarity search techniques. With Retrieval-augmented Generation pipelines, efficient text chunks can be retrieved and further manage the llm tokenization constraints of the LLM and focus on the most relevant and important information. When combined with chunking, this technique becomes even more powerful, as embeddings for individual chunks can be stored and searched independently, allowing for fine-grained retrieval.

Summarize

Text can convey meaning in multiple formats. A lengthier corpus may not necessarily add value to the text’s overall meaning. Summarize your text in a way that fits within the token limit of the model and retains its valuable information.  By condensing the input text into a concise summary, the core information is processed without exceeding the llm token limits. This is effective but can at times skip minute details in the original text. The following examples portray how intelligently summarizing text can solve LLM problems.

Long_text = “It’s such a fine day today, The sun is out, and the sky is blue. Can you tell me what the weather will be like tomorrow?”

Short_text = “It’s sunny today. What will the weather be like tomorrow?”

Shorter_text = “Tell me the weather forecast for tomorrow”

The three versions of the text each ask the LLM about the weather forecast tomorrow in three different ways. Notice how the `Short_text` and `shorter_text` ask the same question but with significantly fewer tokens. This way, text can be summarized to get relevant outputs.

Remove Redundant Terms

Stop word removal is a common technique in NLP to reduce the corpus size. Stop words include meaningless terms like “to” and “the” often appearing in the text. These are important for sentence formation, but modern LLMs focus more on key terms. We can simply write the following terms

“Weather forecast tomorrow Texas”

The LLM will analyze the terms and pick out actions closest to these entities. It will have enough information to understand that you are asking for tomorrow’s weather forecast in Texas.

However, this method is not reliable with complex sentences. Before moving on with this technique, it should be manually verified that the sentence makes enough sense to convey its true meaning. Otherwise, the corpus will render incorrect results.

The Python library NLTK provides a helpful collection of stop words for removal.

import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize 

nltk.download('stopwords')
nltk.download('punkt')

long_sentence = "It's such a fine day today, The sun is out, and the sky is blue. Can you tell me what the weather will be like tomorrow?"

word_tokens = word_tokenize(long_sentence)

short_sent = ' '.join([t for t in word_tokens if t not in stopwords.words('english')])

print(short_sent)

Fine-tuning Language Models

Fine-tuning refers to training a model to perform better on niche tasks. A fine-tuned LLM will produce better results for specific problems with lesser input data; hence it can be used within the token limitation range. Fine-tuning can improve the context window via techniques such as Positional Interpolation to solve the llm token limit.

Fine-tuning involves using the existing weights of a model and continuing to train it with specific data. The model develops a richer understanding of the new information and performs well for similar cases. Popular LLMs like ChatGPT provide guides for fine-tuning their models. Moreover, HuggingFace provides an easy, no-code tool called AutoTrain for LLM fine-tuning. The tool allows you to select relevant parameters and the desired open-source model from the Hugging Face Hub.

For NLP-related training, large text collections are broken down into simpler forms called tokens. A token can be a word, punctuation, part of a word, or a collection of words forming a partial sentence. These tokens are converted into an embedding which the model processes to understand the text.

LLM token limits remain a barrier in 2025, especially for long or multi-file inputs. These limits are defined to ensure model efficiency and computational stability. However, these limits can also prevent full-context processing, which is necessary in legal or technical fields. Fortunately, novel techniques have been developed to mitigate these limitations. Fine-tuning, semantic chunking, summarization, and even parallel processing now offer practical solutions for managing LLM token size constraints.

Developers can now track and optimize token usage using an LLM token counter and real-time dashboards, such as Deepchecks LLM Evaluation, to ensure models stay within limits without compromising response quality.

How do LLM token limits impact real-world applications and model performance?

Token limits mean that an LLM can only look at a specific amount of information. This makes long documentation or legal analysis less accurate. It can make things less clear and give you incomplete results.

How does fine-tuning a language model contribute to solving token limits?

Fine-tuning teaches a model how to operate well with shorter inputs that are specific to a subject. Therefore, fewer large prompts are required, and fewer LLM tokens are used in total.

What is the role of summarization in addressing token limits?

You shorten material when you describe it, yet you preserve its meaning. This ensures that essential points are retained without exceeding the token limit, which is needed for chat-based or RAG systems.

How can I manage LLM token limits effectively?

You should employ token counts, chunking, and a summary. Set the most enormous LLM token size that can be used in a query. Then, use vector search to extract only the necessary data. Finally, use assessment tools like Deepchecks to monitor the model’s performance and look for drift or hallucination.