An intelligent batch-processing engine that fragments large language model prompts into parallel sub-tasks, submits them to the Anthropic API at reduced cost, and reassembles results with contextual coherence.
Inspired by the cost-saving architecture of batch API offloading, PromptSlicer is a purpose-built tool for developers, researchers, and content teams who need to process massive volumes of structured text—documentation analysis, code review at scale, multilingual translation of entire websites, or archival data extraction—without burning through API budgets. Instead of treating the API as a single-stream processing pipeline, PromptSlicer divides your workload into semantically-aware chunks, dispatches them concurrently via the Batch API, and intelligently stitches outputs back into a unified result, preserving cross-referencing, tone, and logical flow.
✦ Why PromptSlicer Exists
The Anthropic API is powerful, but when you're processing a 10,000-page technical manual, a corpus of 50,000 customer support tickets, or an entire codebase spanning hundreds of files, sending everything as a single prompt is impractical—costly, time-consuming, and prone to context-window limits. Standard batching reduces cost by 50%, but it treats each request in isolation. PromptSlicer adds a third layer: fragment-aware orchestration. It understands that chunk 47 of your input depends on definitions introduced in chunk 3, and it ensures the final output reads as a single, coherent document.
Table of Contents
- Overview
- Core Architecture
- Key Features
- Getting Started
- Configuration Guide
- Use Cases & Workflows
- Performance Benchmarks
- Multilingual & Global Readiness
- API Reference
- Project Structure
- Contributing
- License
- Disclaimer
📖 Overview
PromptSlicer is not just a batch submission tool—it is a cognitive load distributor. Imagine you have a 500-page policy document that needs to be summarized, key terms extracted, and translated into six languages. A naive approach would be to send each page as a separate API call, losing cross-references, or send the entire document as one massive context, exceeding token limits and costing a fortune.
PromptSlicer operates like a skilled librarian: it reads the full document once to build a semantic map, intelligently splits it into overlapping fragments (each with sufficient surrounding context to maintain meaning), submits them as parallel batch jobs at the reduced Batch API rate, then reassembles the responses with deduplication, conflict resolution, and narrative stitching. The result is a 50% reduction in token cost per job, a 3–5x speed improvement over sequential processing, and output quality that rivals single-prompt processing.
🧩 Core Architecture
PromptSlicer is built on three modular components that work in sequence:
1. Fragment Engine
The Fragment Engine analyzes your input using a lightweight semantic segmentation algorithm. It identifies natural breakpoints (paragraph shifts, section headers, code block boundaries) and creates overlapping chunks with configurable context windows. Each chunk carries a "context envelope"—metadata about the surrounding 2–5 chunks—so the downstream API calls can reference information from adjacent fragments without requiring the full document in every request.
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