Why This Matters

Large documents present a formidable hurdle for today’s Large Language Models. Imagine you’ve just downloaded a groundbreaking research paper, a dense legal contract, or the latest bestseller—and you want your AI assistant to help you understand it. Unfortunately, those models have strict limits on how much they can process at once. When you feed them an entire book, a sprawling codebase, or a multi-chapter technical manual, they simply can’t accommodate all of it in a single request. That means crucial details at the beginning might be trimmed off when you submit the end, or vice versa, leaving gaps in the AI’s understanding.

Getting it wrong…

In the real world, those gaps can have serious consequences. A researcher trying to synthesize findings across multiple papers may miss key experiments or conclusions if any section falls outside the model’s window. Lawyers and paralegals reviewing contracts risk overlooking a vital clause when the document is truncated. Product teams scanning customer feedback reports may misinterpret sentiment if the AI only sees fragments of longer comments. And in academia, a graduate student compiling a literature review could draw flawed connections if entire paragraphs vanish before the analysis even begins.

When you don’t handle large documents thoughtfully, the fallout goes beyond mere annoyance. Information loss becomes a genuine threat—critical context evaporates, analysis remains incomplete, and misinterpretations creep in. You waste precious resources on failed API calls and redundant computations, driving up costs without delivering value. And the end users—whether they’re executives, developers, or students—lose faith when the AI spits out inaccurate or disjointed content. Poor user experience can rapidly erode confidence in the very tools designed to accelerate our work.

How to get it right…

Yet when you get it right, the difference is transformative. By chunking and recombining text intelligently, you preserve every insight hidden within the pages of even the longest document. The AI can maintain context across sections, delivering coherent summaries, comprehensive analyses, and precise answers. You optimize your usage of compute resources—no more wasted tokens or repeated requests—so processing becomes faster and more cost-effective. And the quality of results soars: every nuance stays intact, every connection remains visible, and every stakeholder from researcher to legal counsel benefits from a seamless, reliable workflow. In short, a thoughtful approach to handling large documents turns what was once a crippling limitation into a well-managed strength.

Digging in: Understanding Token Limits

What are Tokens?

• Tokens are pieces of text that AI models can understand
• A token can be a word, part of a word, or a punctuation mark
• Different models use different tokenization methods

Setting Token Limits

# Defining the max tokens to avoid error for context being too long
encoding = tiktoken.encoding_for_model("gpt-4o-mini")
MAX_TOKENS = 127500

The Problem with Large Documents

When a large document arrives unprepared, the model will attempt to ingest every token at once—and quickly hit its limit. At that moment, you’ll see the dreaded “context too long” error, and the entire request is rejected. It’s as if you tried to pour an overflowing bucket into a narrow funnel: nothing gets through.

Even a naïve workaround—simply truncating the text to the first 127,500 tokens—only shifts the problem. The model will happily process that initial chunk, but everything that follows is silently chopped off. In practice, this means you might never see the critical conclusions buried near the end of a report, the pivotal clauses hidden in the middle of a contract, or the innovative ideas tucked away in later chapters. Information loss becomes inevitable, and insight vanishes with it.

Better Solutions for Large Documents

1. Chunking Strategy

def process_large_document(document, chunk_size=127500):
    # Split document into smaller chunks
    chunks = [document[i:i+chunk_size] for i in range(0, len(document), chunk_size)]

    # Process each chunk separately
    results = []
    for chunk in chunks:
        result = process_chunk(chunk)
        results.append(result)

    return combine_results(results)

2. Sliding Window Approach

def process_with_sliding_window(document, window_size=127500, overlap=1000):
    # Process document with overlapping windows
    for i in range(0, len(document), window_size - overlap):
        chunk = document[i:i+window_size]
        process_chunk(chunk)

3. Summarization Strategy

def summarize_large_document(document):
    # First pass: Create a summary
    summary = create_summary(document)

    # Second pass: Process the summary
    process_summary(summary)

Best Practices

1. Always Check Document Size

def process_document(document):
    token_count = len(encoding.encode(document))
    if token_count > MAX_TOKENS:
        # Use appropriate strategy for large documents
        return process_large_document(document)
    else:
        # Process normally
        return process_normal(document)

2. Document Structure Considerations

When you confront a massive text, the first step is to respect its natural architecture. Instead of feeding an unbroken wall of words into the model, divide the document at logical breakpoints—chapter endings, section headers, or topic shifts—so each piece feels like a coherent mini-document. Within each chunk, carry forward the thread of the narrative or argument by preserving key sentences or terminology that bridge one segment to the next. To guard against losing those threads at the edges, use overlapping windows: let each chunk share a handful of sentences with its predecessor and successor. This way, the model sees a bit of what came before and a preview of what comes after, maintaining continuity and preventing abrupt context drops as you stitch the pieces back together.

3. Information Loss Prevention

Chunking the text is only half the battle—tracking what you’ve already processed is equally crucial. As you feed each segment to the model, keep a running log of the passages it has seen, the summaries it produced, and any flagged highlights. That lets you detect gaps or overlaps and ensures you never inadvertently “skip” an important paragraph. For truly massive works—think multi-volume reports or sprawling codebases—you might layer in executive summaries: generate concise overviews of each section before diving into detail. Those summaries serve as safety nets, capturing the essence of the text in case fine-grained chunks still miss a nuance. By combining diligent bookkeeping with selective summarization, you safeguard against blind spots and make sure every critical insight finds its way into your final analysis.

Conclusion

Understanding and properly handling token limits is crucial for working with large language models. By implementing appropriate strategies and following best practices, you can effectively process documents of any size while maintaining context and preserving important information.

Remember:

• Always check document size before processing
• Choose the right strategy for your use case
• Preserve context and document structure
• Monitor for information loss
• Optimize for performance and resource usage

Resources

Useful Libraries

• tiktoken: For token counting and management
• langchain: For document processing and chunking
• nltk: For natural language processing tasks

Documentation

• OpenAI Tokenizer
• LangChain Documentation
• NLTK Documentation

Appendix: Example Use Cases

1. Research Paper Processing

def process_research_paper(paper):
    # Split into sections
    sections = split_into_sections(paper)

    # Process each section
    results = []
    for section in sections:
        if len(encoding.encode(section)) > MAX_TOKENS:
            # Use sliding window for long sections
            results.extend(process_with_sliding_window(section))
        else:
            # Process normally
            results.append(process_section(section))

    return combine_section_results(results)

2. Long-form Content Analysis

def analyze_long_content(content):
    # Create initial summary
    summary = create_summary(content)

    # Process summary
    if len(encoding.encode(summary)) > MAX_TOKENS:
        return process_with_sliding_window(summary)
    else:
        return process_normal(summary)