The AI Power-User's Dilemma
Big Models, Tiny Prompt Windows
Big Models, Tiny Prompt Windows
Modern AIs like Gemini and GPT-4 possess incredible "context windows"—some capable of processing over 1 million tokens, enough for entire books or codebases in one go!
Yet, their common web interfaces often restrict you to a tiny fraction of this, typically just 30k-130k tokens per prompt.
This "Context Gap" means you can't easily feed your AI the full story it needs for truly deep understanding. How do you get all your data in?
Imagine a tool to process, tokenize, and perfectly portion all your files for any AI web interface…
Stop Wrestling with Prompt Limits.
Start Maximizing Your
AI's Contextual Understanding.
Start Maximizing Your
AI's Contextual Understanding.
Bridge the Context Gap
LMTokenCook is your dedicated desktop assistant designed to process your local files - whether extensive text documents, complex code repositories, or detailed research papers.
Saturate Your AI's Full Memory
It intelligently extracts content and transforms this mass of information into perfectly portioned, token-aware "servings," ready for sequential input.
Achieve Deeper Insights
Feed virtually any Large Language Model web interface with comprehensive context for more thorough analysis and understanding.
Step 1 - Smart Scan & Broad Text Extraction
Versatile Input Options
Easily select entire folders for recursive scanning or specific individual files. Take advantage of convenient drag-and-drop support to quickly add content directly into the application.
Comprehensive File Support
Processes plain text, markdown, source code (Python, JavaScript, Java, C/C++, and many more), data files (.json, .xml, .yaml, .csv), and documents (Word, PDF, Jupyter Notebooks).
Intelligent Filtering
Automatically skips binary files, archives, media files, symbolic links, and commonly excluded development folders (.git, node_modules, __pycache__, etc.) to save time and tokens.
Step 2 - Structure for Clarity, Tokenize with Precision
Automatic File Hierarchy Map
Creates a comprehensive "table of contents" showing the folder structure and all processed files with their token counts, giving the LLM an immediate overview of the dataset.
Clear File Delimiters
Marks beginning and end of each file with visible delimiters (=== File Start: path/file.py ===) to help the LLM distinguish between content from different sources.
Accurate Tokenization
Uses OpenAI's official tiktoken library with cl100k_base encoding - the same used by GPT models and closely aligned with Google's Gemini and Anthropic's Claude tokenization patterns.
Step 3 - Intelligent Token-Based Servings
Set Your Perfect "Bite Size"
Define max tokens per serving based on your LLM's input capacity
Precise Automatic Division
Content split into sequential, manageable text files
Embedded Guidance
Instructions for both user and LLM in each serving
Intelligent Splitting
Preserves integrity by splitting at logical points
These features allow you to conquer web UI prompt limits and empower your LLM to understand the whole story, one perfectly portioned serving at a time.
The Payoff
True Context Augmented Generation!
True Context Augmented Generation!
Deeper Analysis
The LLM conducts more thorough and insightful analysis of large documents, research papers, or legal texts with access to all relevant details.
Better Code Understanding
Developers can provide entire codebases, enabling the LLM to understand interdependencies, generate relevant code, and offer accurate refactoring suggestions.
Coherent Writing
For creative projects, feeding all preceding chapters helps maintain plot consistency, character voice, and thematic coherence in generated content.
Complete Answers
Get responses that are comprehensive, well-reasoned, and directly informed by all your specific data, not just general pre-trained knowledge.
Fine-Tune Your Output
Options for Every Need
Optional Master File
Choose whether to keep the single, large masterfile containing all concatenated content. Useful for smaller contexts or archival purposes.
Line Numbering
Automatically prepend 4-digit line numbers to every output line for precise referencing of specific code, clauses, or passages in your prompts.
Skip Empty Lines
Remove all blank lines from output to create more compact, token-efficient text, especially helpful with inconsistently formatted source files.
Organized Output
Automatically creates timestamped subfolders for each processing run, keeping different jobs separate and preventing accidental overwrites.
Why Choose LMTokenCook
For Your AI Workflows?
Maximize Your LLM's True Value
Overcome restrictive prompt input limits of web interfaces to utilize more of the powerful capabilities that models like GPT-4, Gemini 1.5 Pro, and Claude 3 are designed for.
Completely Local & Private Processing
All file scanning, extraction, tokenization, and output generation happen 100% locally on your computer. Your data never leaves your system until you choose to share it.
Intuitive, Efficient Experience
Clean GUI with drag-and-drop input, real-time progress updates, clear status messages, and helpful options makes preparing your data smooth and efficient.
Accurate & Relevant Tokenization
Using OpenAI's official tiktoken library provides counts that are highly accurate for today's leading LLMs, ensuring your servings fit within prompt limits.
Ready to Supercharge Your AI Interactions?
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Platform Options
LMTokenCook is available for both Windows and macOS
100%
Local Processing
Your data stays completely on your computer
24/7
Support Available
Email support@dropshockdigital.com for assistance