From Pilot to Scale: Ways to Continuously Optimize Your Business AI Chatbots

LLM Routing, Caching, and Data Clustering & Visualization

scale ai chatbot


As businesses increasingly recognize the potential of generative AI, many have launched custom AI chatbots to enhance team productivity and customer experience, aiming to gain a competitive edge. These chatbots are transforming various sectors, including:

  • Customer Service: Offering 24/7 assistance, reducing response times for common queries, and providing support during off-hours.
  • Internal Knowledge Management: Helping employees quickly access relevant company information, such as policies, marketing and sales materials, and product details.
  • Ecommerce Order Management and Product Recommendations: Assisting customers with order tracking, delivery terms, refund policies, and personalized product suggestions.
  • Appointment Scheduling: Streamlining the process of scheduling meetings without human intervention.
  • Self-Learning: Providing customized, interactive courses for learning new skills or languages.
  • Virtual Personal Companions/Assistants: Offering “AI friends” or therapy chatbots for mental health support, thanks to advanced NLP capabilities.

As these initiatives move from development to production, the volume of data processed by your AI chatbot will increase, presenting new use cases, challenges, and opportunities. You may encounter bottlenecks that hinder the scalability of your AI chatbot, potentially derailing your AI projects from their original goals and diminishing competitive advantages or the profitability. Therefore, it’s crucial to evaluate whether these AI chatbots meet initial expectations and to iteratively adjust to achieve a real competitive advantage. 

Key challenges in running and scaling business AI chatbots

1. Select the right LLM models for your unique queries

You may have used LLM benchmark leaderboards like Chatbot Arena or opted for one of the largest and generally best models like GPT-4 to build your chatbot to ensure high performance. However, benchmarks have limitations due to the diverse nature of language tasks. For instance:

  • No single benchmark for all queries: Various benchmarks focus on different language tasks such as answering questions, summarizing text, retrieving information, analyzing sentiment, and modeling language. This diversity means that no single model can be the best for all types of prompts. To achieve optimal performance, you need to select the best model for each of your prompts.
  • Your prompts are unique: LLM benchmarks use specific datasets to test the performance of LLMs on certain tasks and questions. However, these testing datasets can be very different from your actual queries. This means the best model according to a benchmark may perform very differently on your queries. For example, a survey showed that most users prefer using chatbots over human agents for simple requests like tracking order status, searching for products, and getting information on deals. If most of your queries are simple requests, a large model might be overkill. Instead, a smaller model could be a more cost-effective solution without compromising performance. So it’s crucial to select LLM models based on your unique queries and common user interaction patterns to achieve optimal performance and profitability.

After selecting your initial LLM model, you may continue to evaluate new LLM models against your chosen one, seeking to further optimize performance. However, if these evaluations are based on generic use cases instead of specific query-level comparisons, the limitations of generic benchmarks will still apply. Ideally, queries should be routed to the most suitable model on a query-by-query basis to achieve the best results.

2. Optimize costs for scalability and profitability

The goal of implementing a business AI chatbot is to create extra value and gain a competitive advantage by streamlining processes for higher productivity and/or attracting more customers with a better buying experience. This means it’s essential to manage your AI project costs to ensure they don’t diminish or offset the incremental value created. Pay attention to the following two cost areas:

  • Run costs: These are often higher than build costs because of foundation model usage and associated labor costs
  • LLM model inference costs: Reducing model inference costs is an ongoing process. While large models like GPT-4 ensure high performance, smaller models can offer comparable output at a reduced cost and faster execution.

3. Ensure chatbot accuracy

Accuracy is paramount for maintaining user trust and satisfaction, driving actual value. Whether fine-tuning your model or implementing retrieval-augmented generation (RAG) techniques, continuous assessment of output accuracy is necessary. Swiftly identifying and rectifying inaccuracies in your chatbot’s responses caused by poor training data, an outdated RAG knowledgebase, or LLM hallucinations, is vital.

4. Maintain visibility into user interactions

Understanding how users interact with your chatbot is critical for assessing its real value and optimizing resource allocation. Insightful visibility into these interactions allows for effective strategic planning and ensures that resources are directed to the most impactful areas.

Overcoming the challenges with Aguru

Aguru is designed to help businesses optimize their LLM usage and management, ensuring efficient operation and scaling of their AI applications. Our solutions address several key areas in scaling and optimizing your business AI chatbot:

1. Apply the most suitable LLM model for each of your prompts

Aguru’s cluster-based LLM Router goes beyond generic benchmarks and adds extra flexibility to your chosen LLM model for optimal performance. It allows you to add a list of your preferred models on top of your benchmarked best LLM model, which serves as the gold standard. The router instantly evaluates these models against the gold standard and routes queries to the most suitable model on a query-by-query basis, based on your pre-defined performance and cost ratios. Additionally, our LLM Router provides full transparency, enabling you to make confident decisions.

When the router is deactivated, you can see how different LLM models perform on the same queries without automatic routing to different models. This visibility allows users to observe performance and cost metrics firsthand and then decide with confidence whether to activate the router.

Once activated, the LLM Router automatically routes each query to the most suitable model based on your predefined cost-performance balance, ensuring optimal accuracy and reliability. 

Learn more about how our Cluster-based LLM Router works in detail

2. Reuse LLM responses for enhanced cost-efficiency

Aguru’s efficient LLM Caching solution significantly reduces unnecessary expenses for AI applications that frequently handle repetitive queries. By reusing past LLM outputs for similar new prompts through semantic search, it ensures an optimal balance of quality and cost. This approach not only reduces computational resources but also accelerates response times and manages throughput constraints.

3. Better visibility and enhanced insights for chatbot enhancement

Aguru’s advanced Data Clustering feature transforms your large, unstructured prompts into semantically similar clusters, each representing a meaningful user interaction pattern. Additionally, a ‘noise’ cluster groups all the semantically diverse queries, helping you quickly identify outliers and anomalies to improve your AI chatbot accuracy. It also visualizes all the clusters in an intuitive graph that displays inter-cluster relationships and qualities, providing a well-structured overview of your data. This allows you to rapidly dive into specific clusters for targeted insights. Furthermore, Aguru offers insights into your spending and the performance scores of various LLM models on each cluster, providing extra visibility into budget allocation and opportunities for improvement.

In a few words

As your AI chatbot moves from pilot to full-scale deployment, you’ll encounter a new set of challenges that need to be tackled. The end goal is all about ensuring the scalability and profitability of your AI chatbots to gain a real competitive edge. Fortunately, there are solutions designed to help you address these challenges. Solutions like Aguru, designed for LLM usage optimization, can make a significant difference. By leveraging Aguru’s LLM Routing, Caching, and Clustering solutions, you can efficiently select the most suitable model for each of your queries, optimize inference costs, improve chatbot output accuracy, and maintain visibility into user interactions.


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