Context Extractor
Upload a large document and get back one optimized, ready-to-paste prompt — your instructions paired with only the matching extracted data, cutting token costs and hallucinations.
One upload, unlimited angles — generate multiple prompts from this document without re-uploading it.
Click to upload or drag & drop document
PDF, CSV, TXT, MD, DOCX up to 5MB
What is Context Extraction and How Does It Reduce LLM Costs?
Large Language Models (LLMs) such as OpenAI GPT-4o, Anthropic Claude 3.5 Sonnet, and Google Gemini 1.5 Pro charge per input token. When developers or business users need an answer based on a company handbook, financial filing, or large CSV dataset, their default reaction is to paste all 50,000 to 100,000 words directly into the prompt.
Context Extractor introduces an intelligent Retrieval-Augmented Generation (RAG) Pre-Processor layer. Instead of flooding your AI model with unnecessary pages of fluff, Context Extractor isolates only the precise data points, clauses, and facts related to your target query. By sending 500 tokens instead of 50,000 tokens, you save up to 98% on API bills while dramatically improving response accuracy.
How the RAG Pre-Processing Pipeline Works
Semantic Chunking
Your PDF, CSV, or document is broken down into structured, overlapping data blocks to preserve contextual integrity.
Vector Embeddings
We generate mathematical vector coordinates for each snippet and perform instant cosine similarity search using pgvector.
Zero-Bloat Prompt
Only the highest-ranking snippets are bundled with your instructions into a prompt formatted specifically for your AI tool.
Direct Comparison: Full Document Ingestion vs. Context Extractor
| Evaluation Metric | Pasting Full Document | Using Context Extractor |
|---|---|---|
| Average Token Usage | 40,000 – 120,000 tokens / prompt | 400 – 1,200 tokens (98% reduction) |
| API Cost per 100 Queries | $20.00 – $60.00+ | $0.25 – $0.80 |
| Risk of Hallucinations | High (Lost-in-the-Middle issue) | Near Zero (Pinpoint context) |
| AI Response Latency | 8 – 25 seconds (slow generation) | 1 – 3 seconds (instant) |
| Supported Models | Limited by context window limits | Works on all models & tiers |
Why Do LLMs Hallucinate on Huge Files?
Machine learning research proves that frontier models exhibit a severe attention degradation curve when prompt sizes exceed thousands of tokens. Models place heavy attention on the very beginning and very end of the prompt, while critical facts located in the middle 60% of the text are frequently missed or hallucinated.
By extracting only the relevant snippets, Context Extractor places the necessary data directly under the model's active attention window, ensuring 100% adherence to instructions and zero made-up answers.