OptiSave

OptiSave is an advanced AI-driven tool meticulously designed by LLUMO AI’s in-house AI engineers to optimize and streamline the performance of Large Language Models (LLMs). It acts as a powerful intermediary between AI users and LLMs, ensuring that interactions with the model are more efficient, cost-effective, and precise.

At LLUMO AI, we are committed to helping businesses to build trustworthy, high-performance, and cost-effective AI solutions. Our research team works tirelessly to assist organizations in overcoming their biggest hurdles related to generative AI evaluation, experimentation, monitoring, and optimization. We understand the challenges involved in deploying AI at scale, and OptiSave was designed specifically to alleviate these complexities by making AI interactions more seamless and resource-efficient.

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Days of Struggle, Moments of Triumph: The Birth of OptiSave

OptiSave is the answer to the inefficiencies you’ve been battling. The skyrocketing token usage. The frustrating delays. The costs that just wouldn’t quit. We fixed it.

Modern AI applications demand high efficiency, but interacting with LLMs can often lead to excessive token usage, increased latency, and higher operational costs. OptiSave addresses these challenges by compressing input prompts, optimizing token usage, and improving latency, overall model responsiveness by reducing hallucination — without sacrificing one ounce of accuracy. And we did it all without breaking a sweat.

See what’s possible when perseverance meets innovation—when the impossible becomes achievable—look no further than OptiSave.

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Core Objectives of OptiSave:

OptiSave is designed to address several key challenges faced by businesses when interacting with Large Language Models (LLMs). Its core objectives focus on optimizing AI model interactions by improving efficiency, reducing costs, and enhancing the overall quality of generated outputs. Below is a detailed breakdown of each core objective:

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1. Upto 80% Cost Reduction:

One of the primary objectives of OptiSave is to compress input prompt usage while retaining the full meaning of the original input prompt. In the context of LLMs, a “token” represents a unit of text, and the more tokens an input contains, the greater the computational resources required to process it. Tokenization can increase the cost of API usage and may lead to slower processing times if the inputs are too large.

API costs for using LLMs are often directly tied to the number of tokens sent to and generated by the model. Since large inputs and long outputs can quickly add up in terms of cost, minimizing token usage is a significant concern for businesses operating on a budget.

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How Cost Reduction Works:

OptiSave employs advanced techniques like Token reduction, cache management and quality management to compress the input prompt. This involves identifying and eliminating redundant words, phrases, or parts of the input that do not contribute significantly to the model’s understanding.

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How LLUMO AI Optimizes Tokens & Saves Costs at Scale

What is a Token & How is it Calculated?

A token is a unit of text processed by a language model. It can be a word, a part of a word, or even a punctuation mark.

LLMs charge based on the number of tokens used per query, meaning longer prompts = higher costs. Optimizing token usage can drastically reduce your expenses.

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Example

Without OptiSave
Query: “Provide a comprehensive report on the impact of artificial intelligence across industries, including automation trends, economic changes, workforce adaptation, regulatory challenges, and ethical considerations. Also, include case studies, expert opinions, and predictions for the next decade.”
Context: “Knowledgebase”
Input Tokens = Tokens in “Query” + “Context”
Assuming Input Tokens = 2,000

With OptiSave
Precise Query: “Summarize AI’s impact on industries, including automation, economy, workforce, regulations, and ethics.”
Precise Context: “Knowledgebase”
Input Tokens = Tokens in “Precise Query” + “Precise Context”
Input Tokens using OptiSave = 873 tokens.

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Token Reduction & Cost Savings

• 2,000 tokens → 873 tokens (56.35% reduction)
• Assumed cost per token: $0.0001

Without Optisave:
2,000 tokens × $0.0001 =$0.20 per query

With Optisave:
873×0.0001=$0.0873 per query

Total Cost Savings Per Query:
0.20−0.0873=$0.1127 per query (56.35% reduction)

Impact at scale
If running 500,000 queries per month:

• Without Optisave:
  500,000×0.20=$100,000 per month
• With OptiSave:
  500,000×0.0873=$43,650 per month

Total Monthly Savings:
100,000−43,650=$56,350 saved per month **Annual Savings:** 56,350×12=$676,200 saved per year

LLUMO AI helps teams experiment, observe, and optimize LLM usage—so you get high-quality outputs at a fraction of the cost.
See how real teams are cutting costs with LLUMO AI: https://www.llumo.ai/case-study
#AI #LLMOps #CostOptimization #GenerativeAI #TokenEfficiency #AIOptimization

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Benefits:

• Lower Operational Costs: By cutting down on token usage, OptiSave directly lowers the API costs associated with each interaction.
• Reduced Token Usage: By compressing inputs without losing meaning, OptiSave ensures that fewer tokens are sent to the model.
• More Predictable Budgeting: With reduced token consumption, businesses can more accurately forecast and manage AI-related expenses.

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2. Improved Latency:

Response time is a critical factor for many real-time AI applications. High latency can be frustrating for users and can impact the overall user experience, especially in interactive environments like chatbots or customer support systems. Reducing the time it takes for an AI model to generate a response is crucial for improving performance.

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How Latency Optimization Works:

OptiSave addresses latency by reducing computational overhead. By compressing inputs and eliminating unnecessary complexity, OptiSave allows the model to process requests faster. Furthermore, the tool intelligently refines input prompts to ensure that the model’s response generation process is as streamlined as possible, minimizing the time required to generate a high-quality output.

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Example: Improve Latency with Optisave

Without Optisave:
Query: “Provide a detailed technical breakdown of quantum computing, including superposition, entanglement, and qubits, along with its potential applications in cryptography, medicine, logistics, artificial intelligence, and other industries. Additionally, discuss the challenges, future research areas, and expert predictions for the next two decades.”
Context: “Knowledgebase”
Input Tokens = Tokens in “Query” + “Context”
Assuming Input Tokens = 2,000
Processing Time: 10 seconds
Tokens Generated: 2,000 tokens

With Optisave:
Query: “Summarize how quantum computing works and its key industrial applications.”
Context: “Knowledgebase”
Input Tokens = Tokens in “Query” + “Context”
Assuming Input Tokens = 873
Processing Time: 5 seconds
Tokens Generated: 500 tokens

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Latency & Efficiency Gains

• Processing Time Reduced by 50%
  10 seconds → 5 seconds
• Tokens Reduced by 56.33%
  2,000 tokens → 873 tokens

Latency Improvement:
Response time reduced by 50%, from 10 seconds to 5 seconds.

Cost of Delay:
In real-time applications like customer support chatbots, reducing response time by 5 seconds can improve engagement and reduce user frustration.
For 100,000 interactions per day, faster response times lead to increased user satisfaction and better engagement.

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Benefits:

• Faster Response Times: Reducing the number of tokens to process and streamlining the input prompts results in quicker response times, providing a smoother and more efficient user experience.
• Improved User Experience: In environments where real-time interactions are essential, reducing latency can significantly improve customer satisfaction and engagement.

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3. Reduce Hallucinations:

Hallucinations refer to instances when AI models generate responses that are misleading, inaccurate, or completely false. These errors can be particularly problematic in domains where precision and reliability are critical. Minimizing hallucinations is essential to ensure the trustworthiness and quality of the generated outputs.

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How to reduce Hallucinations:

OptiSave tackles hallucinations by improving the clarity and precision of input prompts. Through advanced prompt engineering, it ensures that the model receives clear, concise, and well-structured input. This clarity reduces the likelihood of misinterpretation by the model, which is one of the common causes of hallucinations. Additionally, OptiSave may incorporate mechanisms to provide models with more specific context or grounding information, thereby reducing ambiguity and improving the accuracy of responses.

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Example:

Without Optimization:
Query: “Tell me about the economic impacts of the Mars rover.”
Output: “The Mars rover has significantly impacted global economies by leading to technological advancements in several industries.”
Hallucination: The economic impact of the Mars rover on global economies is not a verified fact.

With OptiSave Optimization:
Query: “Describe the technological impacts of the Mars rover on various industries.”
Output: “The Mars rover led to innovations in robotics and materials science, which have applications in industries such as aerospace and healthcare.”
Accurate Output: This response is factually grounded and accurate.

Cost of Hallucinations:
By reducing hallucinations by just 5%, you can significantly improve the reliability of your AI outputs, saving businesses potentially millions of dollars in miscommunication costs.

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Benefits:

• Enhanced Accuracy: Clearer and more specific inputs reduce the likelihood of the model generating inaccurate or misleading outputs.
• Increased Trust: By minimizing hallucinations, OptiSave helps ensure that AI outputs are more reliable, which is particularly important in professional settings where accuracy is paramount.

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How to Use OptiSave

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Prerequisites:

Make sure you are in the Experiment section of the product and have some data ready for testing (either your own data or sample data).

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Step 1: Access OptiSave

Click on OptiSave on the right-hand side (RHS) of the Experiment section.
A new side menu will appear.

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Step 2: Configure Your Column

Enter a Column Name to label your experiment.

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Step 3: Choose Your Prompting Method

You have two options:

• Write Prompt – You can manually enter a prompt to generate responses.
• Smart Prompt – This option enables automatic prompt optimization.
  You can select both checkboxes if you want to generate two separate columns using both methods.

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Step 4: Select Provider & Model

Under Select Provider, choose the AI provider (e.g., OpenAI).
Under Select Model, pick the AI model you want to use.

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Step 5: Run the Experiment

Click on Create And Run All Column to execute the experiment.

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Step 6: Review the Results

Add Image related to this 4 column

After running the experiment, four new columns will be generated in the experiment table:

• Compressed Input – An optimized version of the original input prompt.
• Output – The response generated via OptiSave.
• Cost – The cost incurred for generating this output.
• Saving – A percentage comparison of cost savings with and without OptiSave.

Now, you’re all set to optimize and analyze your AI-generated responses efficiently with OptiSave!
By following these steps, you can seamlessly integrate and measure the impact of OptiSave in optimizing your AI models for performance, cost-efficiency, and response quality.

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FAQs

1. What is OptiSave used for?
   OptiSave is designed to optimize prompts, reduce costs, and improve AI efficiency through automated compression techniques.

2. Can I use OptiSave with any model?
   Yes, OptiSave supports various LLMs and can be applied across different AI models.

3. How does OptiSave help in cost reduction?
   By compressing input prompts, OptiSave minimizes token usage, leading to lower processing costs without sacrificing performance.

4. Will OptiSave affect output quality?
   No, OptiSave is designed to maintain or even enhance output quality while optimizing efficiency. With reduced input tokens, content becomes more precise, which helps in reducing hallucinations.

5. How does OptiSave help in reducing hallucinations?
   OptiSave refines input prompts and compresses content, ensuring more precise instructions for the model. This reduces ambiguity and minimizes the chances of generating hallucinated or irrelevant responses.

6. How does OptiSave improve latency?
   By reducing input token size and optimizing outputs, OptiSave decreases data processing time. This leads to faster response generation and improved real-time performance.

7. How do I track my optimizations?
   OptiSave provides real-time tracking and logs all improvements in the Experiment section for easy comparison.

By following these steps, users can effectively leverage OptiSave to refine their AI models, enhance performance, and achieve cost efficiency seamlessly.