Save_AI_Cost_Smartly_Ebook

1. Beyond LLMs 10 PROVEN AI COST SAVING HACKS “This guide includes key state-of-the-art cost-saving hacks from top 100+ AI teams to save their LLMs' costs smartly & effortlessly.” Part-1 @llumoai

2. Author’s note Dear Friend, Hope you're doing awesome! We did something super cool —we talked to AI experts from top MNCs like Microsoft, Google, Intel, Salesforce, etc. and got their top secrets on making awesome GenAI stuff. Then, we worked really, really hard to share all that key hacks with you in this guide. Guess what? We don't want to keep it just for ourselves. Nope! We want EVERYONE to have it for free! So, here's the deal: grab the guide, follow us on WhatsApp , and share it with your friends and your team. Let's make sure everyone gets to be a GenAI expert they desire to be for free! Why are we doing this? Because we're all in this together. We want YOU to be part of our GenAI revolution – LLUMO: Let’s go beyond LLMs. Thanks a bunch for being awesome! Catch you on WhatsApp !  Share this EGuide! 2 www.llumo.ai

3. Contents 1. What you’ll learn? 4 2. Is it the right fit for you? 5 3. Key challenges of LLMs powered chatbots 6 4. What are the cost centers in your LLM chatbots 7 5. What are the different ways to cut LLM costs? 10 a) Cost Analytics and monitoring 12 b) Right-sizing your LLM 16 c) Prompt Compression 21 ?? Conclusio? 26 ?? What’s next? 28 Share this EGuide! 3 www.llumo.ai

4. 10 proven AI cost-cutting techniques: Part 1 What will you learn? This comprehensive guide helps you to significantly reduce the cost of building and maintaining chatbots powered by Large Language Models (LLMs). This guide covers in-depth strategies and practical examples for saving costs at different stages of building and inferring a chatbot built on top of LLMs. It is helpful for startups, SMEs, and enterprises alike. It will help you acknowledge the various challenges of using LLMs in chatbots, with a focus on cost considerations. Share this EGuide! 4 www.llumo.ai

5. 10 proven AI cost-cutting techniques: Part 1 Is it the right fit for you? This guide is designed for growth AI teams who want to scale their AI/LLM products without burning a hole into their AI budget, including? ? Developers interested in building cost-effective AI chatbot? ? Teams looking to leverage LLMs for internal or external use cases at the least cost possibl? ? Data scientists seeking to optimize LLM performance and cos? ? Teams using open source and custom LLMs to build universal LLMs grade AI products  By implementing the strategies outlined in this guide, you’ll be well-equipped to build powerful, cost-efficient AI chatbots that enhance customer experience and drive business success. Cut LLM Input Costs by 80% Ensure production models deliver reliable results while optimizing performance with actionable insights. Learn more Share this EGuide! 5 www.llumo.ai

6. 10 proven AI cost-cutting techniques: Part 1 Key challenges of LLMs powered chatbots  ? High Compute Costs: Running LLM computations incurs significant costs due to the massive amount of data processing involved. Each interaction in an LLM-powered chatbot contributes to these costs? ? Bias and Toxicity: Like any AI trained on data, LLMs can inherit biases and toxicity present in that data, leading to skewed or offensive responses. Mitigating bias often requires additional resources or humans in the loop for verification of responses from LLMs? ? Explainability: Understanding how LLMs arrive at their answers can be difficult. This lack of transparency can be problematic for tasks requiring clear reasoning? ? Prompt Engineering: Crafting effective prompts to guide the LLM is crucial for accurate responses. This process can be time-consuming and require expertise, adding development costs? ? Long chat memory: As chatbot usage increases, so do the computational and memory demands. Each inference contains all previous user conversations to bring personalization. This can lead to exponential cost increases. Share this EGuide! 6 www.llumo.ai

7. 10 proven AI cost-cutting techniques: Part 1 What are the cost centers in your LLM chatbots? Context Overload 5% 15% 30% Verbose Prompt Engineering LLM Redundancy for Repetitive Queries View chart 25% Improper LLM Selection 25% Unnecessary Historical Context The Cost Culprits: Understanding Where Expenses Arise in LLM Chatbots LLM-powered chatbots are a powerful and cost-effective tool for customer service or even your internal query resolutions, but at the same time, if improper practices are introduced, it easily becomes a huge cost center for any organization. Here's a breakdown of the primary factors contributing to expense: ? Improper LLM Selection: Selecting an overly powerful LLM for a simple task is akin to using a supercomputer for basic calculations. The high computational resources required by these models lead to significant costs per interaction. Moreover, if you use the same model for end-to- end processing, it will unnecessarily increase your cost as well as inference time. Share this EGuide! 7 www.llumo.ai

8. 10 proven AI cost-cutting techniques: Part 1 ? LLM Redundancy for Repetitive Queries: Many users ask similar questions, leading to redundant LLM computations. Each time a user inquires about the same topic, the LLM performs calculations afresh, even if the answer already exists. Moreover, it also brings in the problem of consistency in the outputs as LLMs might generate different outputs for the same query leading to confusion in users? ? Context Overload: Document-based chatbots often transmit excessive contextual data to the LLM, even information irrelevant to the current query. This data glut increases processing time and associated costs. Additionally, the LLM might struggle to identify the crucial details amidst the noise, potentially leading to inaccurate responses - “Lost in the middle”. Inefficient context management forces the LLM to process unnecessary data, impacting both cost and accuracy. Cut 80% AI cost for free We compress tokens & AI workflows. Plug in and watch your LLM costs drop 80% with 10x faster inference. LLUMO is free till we save you $100k in LLM spend. Limited time offer! Learn more Share this EGuide! 8 www.llumo.ai

9. 10 proven AI cost-cutting techniques: Part 1 ? Verbose Prompt Engineering: Crafting prompts for the LLM can be time-consuming. Poorly written prompts can be overly detailed and lengthy, increasing the number of tokens the LLM needs to process. This translates to higher computational demands and expenses. In many cases, it can confuse the LLMs as well when you write the same instructions in different ways at different points of the prompt? ? Unnecessary Historical Context: Including the entire chat history in context for every new question burdens the LLM. This vast amount of data significantly increases the processing load, leading to exponential cost growth with each interaction. Most of these chat history also act as noise to current query deeming it completely unnecessary. Share this EGuide! 9 www.llumo.ai

10. 10 proven AI cost-cutting techniques: Part 1 What are the different ways to cut LLM costs? Saving-0% Cost- $100 Without any technique With LLUMO Total Saving-96% Cost- $4.64 upto 96% Cost- $5.16 Cost- $10.32 Cost- $12.90 Cost- $16.13 $ Savings Cost- $20.16 Cost- $33.6 Cost- $40 Cost- $60 Saving-0% Cost- $100 Cost Right-sizing Chained Model Fine- Caching Context Content Prompt Memory Analytics LLM Models Routing tuning Management Reduction Compression Management Taming the Cost Monster: Essential Techniques for LLM Chatbot Optimization Building cost-effective chatbots requires a strategic approach. Here are ten essential techniques to optimize your LLM chatbot and ensure it delivers both exceptional experiences and budget-friendly operations: ? Cost Analytics and Monitoring: Track your LLM usage to identify areas for cost reduction.? ? Right-sizing Your LLM: Choose the most cost-effective LLM based on task complexity. This typically saves up to 40% of the cost. Share this EGuide! 10 www.llumo.ai

11. 10 proven AI cost-cutting techniques: Part 1 ? Chained Models: Break down tasks into sequential steps using different models, balancing accuracy and cost. This usually results in a cost savings of around 20%? ? Model Routing: Direct tasks to the most suitable LLM based on the nature of the inquiry. This typically leads to a cost reduction of about 30%? ? Fine-tuning Pre-trained Models: Improve task-specific performance with less context (lower cost) by fine-tuning pre-trained models with your data. This often leads to significant cost savings, typically around 40%? ? Caching: Store pre-computed responses for frequently asked questions, reducing LLM computations. This can lead to substantial savings, often up to 20% of the total cost? ? Context Management: Provide only relevant context for each query, minimizing LLM processing needs. This can result in considerable cost savings, often as much as 20%? ? Content Reduction: Clean data before feeding it to the LLM, reducing unnecessary processing overhead. This usually results in a cost savings of around 20%? ? Prompt Compression: Compress prompts to make it concise and focused before passing it to the LLM, minimizing token usage and cost. This usually results in cost savings of around 80%? ? Memory Management: Pass only relevant information from the user's chat history for each query. This typically saves up to 10% of the cost. We will now discuss the implementation of each of these strategies in detail. Share this EGuide! 11 www.llumo.ai

12. 10 proven AI cost-cutting techniques: Part 1 Cost Analytics and Monitoring What is it? Cost analytics and monitoring is the practice of tracking and analyzing data related to your chatbot’s LLM usage. As your application grows, you’re going to need a tool to keep an eye on where your costs are coming from. Particularly as part of your overall monitoring strategy, you should catch incidents such as application errors and overactive users abusing your product that you’re going to want to jump on right away. Some metrics that you should track are? ? Normal LLM cost vs compressed LLM cos? ? Average cost per mode? ? Average latenc? ? Percentage of tokens compresse? ? Average cost per interactio? ? Logs of interactions Share this EGuide! 12 www.llumo.ai

13. 10 proven AI cost-cutting techniques: Part 1 How to implement it? Many chatbot development software offer built-in cost analytics dashboards. You can also create your custom dashboards to visualize and track the data more accurately. On top of this, almost all LLM platforms give price calculators. There are some tools like , etc which provide external integration Aporia Props AI , capabilities to track your cost centers in any LLM. If you want identification, tracking and optimization from identification of cost centers, to tracking the token usage per interactions to visualization of inference cost and cost reductions on different steps at one place, is the tool for you. LLUMO AI Scale Conversational AI 10x with data driven optimization Enable your team to fine-tune, monitor, and deploy LLMs at scale while reducing time- to-market by 10x. Learn more Share this EGuide! 13 www.llumo.ai

14. 10 proven AI cost-cutting techniques: Part 1 How does LLUMO make it easy? ? Pinpoint areas where your chatbot is incurring the highest costs. For example, you might discover that a specific type of query is resource- intensive or that your prompts are overly complex? ? Evaluate the return on investment (ROI) of your chatbot by comparing its cost with the value it generates (e.g., increased customer satisfaction, reduced support tickets). Share this EGuide! 14 www.llumo.ai

15. 10 proven AI cost-cutting techniques: Part 1 ? Allocate resources efficiently based on usage patterns. You might identify opportunities to use lower-cost models for simpler tasks or implement caching for frequently asked questions? ? Visualize the trend of your usage on a dashboard and segregate customers or queries or prompts that are spiking the cost. Cut 80% AI cost for free We compress tokens & AI workflows. Plug in and watch your LLM costs drop 80% with 10x faster inference. LLUMO is free till we save you $100k in LLM spend. Limited time offer! Learn more Share this EGuide! 15 www.llumo.ai

16. 10 proven AI cost-cutting techniques: Part 1 Right-sizing Your LLM What is it? Right-sizing your LLM is the process of selecting the most cost-effective LLM based on the complexity of the task your chatbot needs to perform. Imagine using a ferrari to go grocery shopping - unnecessary and expensive, right? 
 The same principle applies to LLMs in chatbots. Choosing the “right-sized” LLM, one that’s powerful enough for the task without being excessively expensive, is crucial for cost optimization. 
 Simply using less powerful, lower-priced models for the right executions in your application can have a 35% or more drop in costs for the given execution. Share this EGuide! 16 www.llumo.ai

17. 10 proven AI cost-cutting techniques: Part 1 How to implement it? Right-sizing your LLM follows the following approach. 
 First, you need to analyze your chatbot’s tasks: Is it answering basic questions or providing in-depth analysis? Analyze the level of understanding, reasoning, and response generation required for each task. 
 Then, you need to research different LLM models, comparing their capabilities and costs – some excel at simple tasks for a lower price, while others offer advanced features at a premium. You need to look for models specifically designed for tasks similar to yours. Through test iterations, you compare accuracy, performance, and cost of each model. You can compare various LLMs on tools like Airtrain Share this EGuide! 17 www.llumo.ai

18. 10 proven AI cost-cutting techniques: Part 1 Finally, you strike a balance: a fuel-efficient car for errands, or a powerful SUV for off-road adventures. By considering task complexity and LLM options, you choose the most cost-effective model that gets the job done. You can check the leaderboard at Huggingface .

 If you want to compare all LLMs for different prompts at one place and that too without ground truth, LLUMO AI is the tool for you. Build Scalable, Reliable AI Models with Custom Evals Test LLMs against your business-specific KPIs and gain 360° insights to drive innovation securely. Learn more Share this EGuide! 18 www.llumo.ai

19. 10 proven AI cost-cutting techniques: Part 1 How does LLUMO make it easy? ? Compare models on different evaluation metrics: With LLUMO AI, evalLM you can select from a series of evaluation metrics based on real world scenarios. You can also create your own metric just by writing your evaluation checklist in plain English. It is 84% consistent with human evaluation, making it the perfect assistant for LLM comparison. Share this EGuide! 19 www.llumo.ai

20. 10 proven AI cost-cutting techniques: Part 1 ? Benchmarking and Testing: Run pilot tests with different LLM models on representative chatbot tasks. Compare the accuracy, performance, and cost of each model? ? Optimized Resource Allocation: You allocate computational resources efficiently, avoiding unnecessary use of high-powered models for simple tasks. Cut 80% AI cost for free We compress tokens & AI workflows. Plug in and watch your LLM costs drop 80% with 10x faster inference. LLUMO is free till we save you $100k in LLM spend. Limited time offer! Learn more Share this EGuide! 20 www.llumo.ai

21. 10 proven AI cost-cutting techniques: Part 1 Prompt Compression Prompt Compression: Streamlining Instructions for Efficient LLM Interaction What is it? Prompt compression is a technique for reducing the size and complexity of prompts used to guide LLMs. It involves removing unnecessary elements while preserving the core instructions and context required for the LLM to generate the desired response or complete the task effectively. It focuses on condensing the prompts you provide to the LLM, ensuring they are clear, concise, and contain only the essential information needed for the task. Prompt compression can help you reduce 60-70% cost on each inference of user query on a chatbot. Share this EGuide! 21 www.llumo.ai

22. 10 proven AI cost-cutting techniques: Part 1 How to implement it? There are several techniques for compressing prompts? ? Identifying Core Instructions: Analyze your existing prompts and identify the essential information the LLM needs to understand the task and user intent. Remove any redundant or tangential details that don't contribute to the core instruction? ? Leveraging Templates: Develop standardized templates for frequently used prompt structures. These templates can be pre- populated with relevant information for each user query, reducing the overall prompt size? ? Removing Unnecessary Information: Instead of describing entities (like products or locations) in detail within the prompt, highlight them directly if their information is not directly required in processing of the LLM and generating the output. Use LLMs for the precision, not the complete generation. Share this EGuide! 22 www.llumo.ai

23. 10 proven AI cost-cutting techniques: Part 1 The additional information about these entities can be appended to your output from your knowledge base or external sources, reducing the need for lengthy descriptions within the prompt itself. You can use LLUMO AI ’s proprietary technology to experience prompt compression at its best. ? Fine-tuning with Compressed Prompts: Fine-tune your LLM specifically with compressed prompts. This helps the LLM adapt to understanding concise instructions and generating accurate responses based on minimal guidance. You can see LLM-Lingua from Microsoft and to see the fine-tuned compressor in LLUMO AI action. Cut LLM Input Costs by 80% Ensure production models deliver reliable results while optimizing performance with actionable insights. Learn more Share this EGuide! 23 www.llumo.ai

24. 10 proven AI cost-cutting techniques: Part 1 How does LLUMO make it easy? Total cost saved LLUMO AI has state of an art prompt, proprietary compression algorithm that has many benefits such as? ? Reduced Processing Costs: Smaller prompts require less processing power for the LLM, potentially leading to lower costs associated with LLM usage. Compressed prompts are generally 60-70% cheaper than actual prompts in each query. Share this EGuide! 24 www.llumo.ai

25. 10 proven AI cost-cutting techniques: Part 1 ? Improved Efficiency: Faster processing due to reduced prompt complexity can translate to quicker response times for your chatbot and a smoother user experience. End users can experience a 60% reduction in inference speed? ? Enhanced Generalizability: Concise prompts tend to focus on core concepts rather than specific phrasings. This can make the LLM more adaptable to variations in user queries and improve its overall performance. Share this EGuide! 25 www.llumo.ai

26. 10 proven AI cost-cutting techniques: Part 1 Conclusion In this part of the ebook on cost-saving techniques in chatbots, we've explored the world of chatbot optimization and discovered powerful techniques to make your AI assistant truly intelligent and cost-efficient. By implementing strategies like caching, prompt compression, and context management , you can unlock significant cost savings, potentially reaching 60-70% reductions! We deep-dived into a few strategies in detail like cost monitoring, prompt compression, and right-sizing your model. Cut 80% AI cost for free We compress tokens & AI workflows. Plug in and watch your LLM costs drop 80% with 10x faster inference. LLUMO is free till we save you $100k in LLM spend. Limited time offer! Learn more Share this EGuide! 26 www.llumo.ai

27. 10 proven AI cost-cutting techniques: Part 1 In the next parts, we will discuss other remaining strategies and go through a case study of a company saving around 100K USD annually by implementing these techniques. If you want to save your LLM costs by 60% in just a few clicks, look no further than LLUMO – your one-stop shop for LLM cost optimization. With 's user-friendly tools, you can transform your chatbot into LLUMO a cost-effective powerhouse, boosting both efficiency and your bottom line. Share this EGuide! 27 www.llumo.ai

28. 10 proven AI cost-cutting techniques: Part 1 What's next? Prompting Smartly Why we built LLUMO? Techniques from successful, high- Why We Built LLUMO: The Story. paid prompt engineers: Examples Tips & tricks @LLUMO blogs Leader Hacks Unveiled Unlock AI Hacks in Our Blogs! Unveiling Success: Top AI Pros Speak! Share this EGuide! 28 www.llumo.ai

29. 10 proven AI cost-cutting techniques: Part 1 Level up with the elite: 1-min quick LLUMO demo Join LLUMO's community AI Talks, Discover LLUMO: 1-Minute Demo! Top Engineer Assistance! Want to remain updated on new GenAI, prompt and LLMs trend? Follow us on social media @llumoai Share this EGuide! 29 www.llumo.ai

30. 10 proven AI cost-cutting techniques: Part 1 Want to cut LLM cost effortlessly? Try LLUMO and it will transform the way you build AI products, 80% cheaper and at 10x speed. Learn more Share this EGuide! 30 www.llumo.ai