MemoryLens

🧠 Overview

Most AI prompts are too long.

In practice, a large portion of tokens does not contribute to the final output. This leads to higher costs, slower responses, and unnecessary complexity.

MemoryLens was built to make this invisible inefficiency visible.

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💡 Inspiration

While analyzing AI-based projects, I noticed recurring issues:

• context window limits
  
• high API costs
  
• latency caused by long prompts
  

The problem was not the model itself, but how prompts were written.

This led to a simple question:

What part of a prompt actually matters?

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⚙️ What it does

MemoryLens analyzes prompts and:

• visualizes token usage
  
• highlights important parts
  
• removes unnecessary content
  
• compares original vs compressed outputs
  

In many cases, prompts can be reduced by 30–50% without changing the result.

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🏗 How I built it

The system is designed to minimize reliance on AI:

• Tokenization for exact token counting
  
• Heuristic-based importance scoring
  
• Web-based dashboard for visualization
  
• Optional LLM usage only for compression suggestions
  

This makes the system fast, stable, and interpretable.

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🚧 Challenges

1. Measuring importance

LLMs do not expose which tokens are actually used.

→ Approximated using:

• position
  
• repetition
  
• structural relevance
  

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2. Maintaining output quality

Compression is only useful if meaning is preserved.

→ Implemented:

• side-by-side comparison
  
• similarity estimation
  

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3. Usability

Token analysis can be hard to understand.

→ Solved with:

• heatmaps
  
• before/after visualization
  
• simple UI flow
  

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📊 Key Insight

Most of the prompt is not used.

MemoryLens turns this into something measurable and actionable.

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🚀 What I learned

• Prompt design strongly impacts cost and performance
  
• Visualization improves understanding of AI behavior
  
• Lightweight systems can outperform complex pipelines in reliability
  

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🔮 What's next

• Embedding-based learning loop: Upgrade from TF-IDF to semantic embeddings with a continuous feedback loop that improves compression accuracy over time.

• LLM-powered verification: Add optional LLM comparison to validate that compressed prompts produce equivalent outputs, moving from estimated to verified compression quality.

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🧩 Why it matters

As AI usage scales, efficiency becomes critical.

MemoryLens helps reduce cost and improve performance without changing the model itself.

Built With

• chart.js
• fastapi
• javascript
• python
• react
• tiktoken