How to Process Huge Documents with LLMs?

Why Processing Large Documents Is a Challenge?

Most LLMs have context length limits (e.g., 8K, 32K, or even 200K tokens).
Sending a full document at once can lead to:

• Truncated input (information cut off before processing ends),
• High costs due to excessive token usage,
• Increased latency,
• Loss of context if the model cannot maintain coherence across sections.

To solve this, you need a structured approach that splits, routes, and processes your document intelligently.

1. Don’t Send the Whole Document at Once

Resist the temptation to feed the full file directly into a single API call.
Instead, break down the document into smaller, manageable sections that can be processed independently.

This approach:

• Reduces API cost,
• Improves reliability,
• Keeps responses coherent and interpretable.

2. Use Chunking and Overlaps

Chunking means splitting a document into smaller text segments.
To ensure context continuity between parts, you can include overlaps, a few sentences repeated between chunks.

Example:

• Chunk 1: Paragraphs 1–5
• Chunk 2: Paragraphs 5–9

That overlap helps the model maintain context flow and prevents loss of meaning.

💡 Tip: Adapt chunk size to your model’s token limit. For instance, 1,000–2,000 tokens per chunk for models like GPT-4-turbo or Claude 3.

3. Split the Work into Different Stages

Not all steps in document processing are equal. Instead of sending everything to a single LLM, divide the workflow into stages:

1. Extraction: Identify structure (titles, sections, metadata)
2. Summarization: Create section summaries
3. Synthesis: Combine partial summaries into a global one

Each stage can reuse outputs from the previous one, making the pipeline modular, traceable, and cost-efficient.

4. Choose the Best Model for Each Task

Different models perform better on different subtasks:

• OCR or Text Extraction: Use specialized vision-to-text or document parsing APIs.
• Summarization: Use a large context or summarization-optimized LLM.
• Classification or Tagging: Smaller, cheaper models are often enough.
• Translation: Use dedicated translation APIs for better accuracy.

By combining multiple models, you get higher quality at lower cost than using one large model for everything.

How Eden AI Simplifies the Process

Eden AI allows you to orchestrate multiple AI models and steps through a single platform:

• Access dozens of AI APIs for extraction, summarization, translation, and classification.
• Use one unified API to chain multiple LLM tasks.
• Automatically route tasks to the best provider based on cost and performance.
• Monitor processing time, cost, and model accuracy.

With Eden AI, you can build robust workflows for huge document pipelines, without writing complex orchestration code.

Conclusion

Processing long documents with LLMs isn’t about sending everything at once, it’s about structuring intelligence.
By chunking, staging, and matching each task with the right model, you can scale document analysis efficiently and cost-effectively.

With Eden AI, you turn complex multi-step document processing into an automated, optimized pipeline, ready for production.

FAQ — Process Huge Documents with LLMs