PromptSlicer 🧠⚡

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.

2. Dispatch Layer

This module handles communication with the Anthropic Batch API. Instead of queuing requests one at a time, it builds a batch manifest containing all fragments, applies rate-limit-aware scheduling, and monitors job status via webhook-compatible polling. It automatically retries failed fragments (up to a configurable limit) and logs every API interaction for auditability. Cost savings are tracked in real-time, displayed as both raw dollar savings and percentage reduction compared to standard API usage.

3. Stitch Subsystem

The Stitch Subsystem is where PromptSlicer differentiates itself. After all batch responses are received, it performs a contextual merge: it aligns responses based on the original fragmentation boundaries, resolves contradictions (if two chunks offer conflicting interpretations, it flags them for human review), removes redundant cross-references, and applies a final consolidation pass that harmonizes tone and style. The output is a single, polished document that reads as if it were generated from a single prompt.

✨ Key Features

🚀 Getting Started

To begin using PromptSlicer, you'll need:

• An Anthropic API key with batch submission enabled
• A text file, directory of text files, or URL pointing to a corpus you want to process
• A clear idea of the target output format (summary, extraction, translation, analysis)

Quick configuration example:

1. Define your source: a local file, a directory, or a public URL
2. Choose your processing mode:
   • summarize — condense each chunk and stitch into a unified summary
   • extract — pull out structured data (JSON schema definable)
   • translate — convert to target language(s), preserving formatting
   • analyze — run a custom prompt template against each chunk
3. Set your context overlap (default: 2 chunks of overlap for coherence)
4. Run the dispatcher and monitor progress

PromptSlicer will output a final stitched document plus a metadata file showing per-fragment costs, processing times, and any conflicts resolved.

⚙️ Configuration Guide

PromptSlicer uses a plain-text configuration approach (no complex YAML required). Key configuration parameters include:

• Source Path — file, directory, or comma-separated list of URLs
• Chunk Size — target token count per fragment (default: 4000; range: 1000–8000)
• Overlap Window — how many adjacent chunks share context (1–5)
• Output Mode — single_document (stitched) or fragments_only (raw batch outputs)
• Target Language(s) — for translation mode, accepts ISO 639-1 codes (e.g., fr,de,ja,zh)
• Custom Prompt Template — optional string with {fragment} placeholder for analysis mode
• Retry Limit — max retries per failed fragment (default: 3)
• Log Level — info, debug, or silent

These settings can be passed as runtime flags or stored in a configuration file for repeatable workflows.

🔄 Use Cases & Workflows

Technical Documentation Processing

Transform a 1,200-page API reference into a structured knowledge base. PromptSlicer fragments by topic area, extracts function signatures and descriptions, and stitches them into a searchable document that preserves cross-references.

Codebase Audit & Migration Analysis

Feed a monorepo's worth of source files into PromptSlicer in extraction mode. Each file becomes a fragment; the Stitch Subsystem consolidates dependency graphs, deprecated function usage, and suggests migration paths in a unified report.

Multilingual Website Localization

Provide a sitemap URL; PromptSlicer fetches each page, fragments by content section, translates fragments in parallel across six languages, then stitches per-language versions with preserved markup (links, headings, lists).

Legal Document Analysis

Process contracts, agreements, or regulatory filings. Configure PromptSlicer to extract clauses, dates, parties, and obligations from each fragment, then stitch them into a normalized summary with conflict flags.

📊 Performance Benchmarks

Benchmarks conducted on a corpus of 100 technical articles (average 2,000 tokens each):

Interpretation: PromptSlicer achieves near-single-prompt coherence at half the cost and four times the speed. The small drop in coherence is context-dependent—overlap windows of 3 or more often eliminate the gap entirely.

🌐 Multilingual & Global Readiness

PromptSlicer's Fragment Engine has been tested with:

• CJK languages (Chinese, Japanese, Korean) — segmentation respects character-based grammar without whitespace boundaries
• Right-to-left scripts (Arabic, Hebrew, Urdu) — text direction metadata preserved through stitching
• Accented Latin scripts (French, German, Spanish, Italian, Portuguese) — diacritics handled with full fidelity
• Cyrillic & Greek — UTF-8 normalization ensures no character corruption

The Stitch Subsystem performs language-appropriate reconstruction: in Arabic, connectors are merged differently than in French. Multilingual output is validated against Google's Natural Language API for consistency.

📡 API Reference

PromptSlicer exposes a lightweight local API endpoint for integration into larger systems:

• POST /process — Submit a job (file path, configuration, target languages)
• GET /jobs/{job_id} — Retrieve job status and progress percentage
• GET /jobs/{job_id}/results — Download stitched output + fragment log
• DELETE /jobs/{job_id} — Cancel a running job

All endpoints return JSON with consistent error structures. Batch submission to Anthropic is abstracted; you never interact with the upstream API directly.

📁 Project Structure

prompt-slicer/
├── fragment_engine/       # Semantic segmentation & context envelope creation
│   ├── segmenter.py
│   ├── overlap_calculator.py
│   └── detectors/         # Language-specific detectors
├── dispatch/              # Batch submission, retry, and monitoring
│   ├── batch_orchestrator.py
│   ├── retry_handler.py
│   └── cost_tracker.py
├── stitch/                # Reassembly, dedup, conflict resolution
│   ├── merger.py
│   ├── conflict_resolver.py
│   └── tone_normalizer.py
├── api/                   # Local REST API
│   ├── index.py
│   └── validators.py
├── config/                # Configuration templates
├── tests/                 # Unit & integration tests
└── README.md

🤝 Contributing

Contributions are welcome in the form of:

• New language detectors for the Fragment Engine
• Conflict resolution heuristics for the Stitch Subsystem
• Performance optimization for very large corpora (1M+ tokens)
• Additional output modes (e.g., structured JSON, Markdown, Notion API export)

Please file issues for bugs or feature requests. Pull requests should include test coverage for new language detectors or stitching algorithms.

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

⚠️ Disclaimer

PromptSlicer is a wrapper and orchestrator for the Anthropic Batch API. It does not modify, intercept, or store API keys beyond local configuration. Output quality depends on the underlying model's capabilities and the configuration of context overlap and chunk size. The user is responsible for ensuring that submitted content complies with Anthropic's Acceptable Use Policy. PromptSlicer is not affiliated with or endorsed by Anthropic. The 50% cost reduction is based on standard Batch API pricing as of 2026 and is subject to change by the upstream provider. Always verify your API usage costs in your Anthropic dashboard.

PromptSlicer — Because not every prompt needs to be a monolith. 🧩