Navigating the Evolution of Large Language Models: Beekeeper’s Dynamic Solution for Frontline Workforce Optimization
Co-authored by Mike Koźmiński from Beekeeper
In this article, we explore the challenges organizations face when selecting and optimizing Large Language Models (LLMs) for various use cases, and how Beekeeper has developed a sophisticated system to address these challenges effectively.
Navigating the Evolving Landscape of Large Language Models: A Solution by Beekeeper
Co-written by Mike Koźmiński from Beekeeper
In the rapidly changing world of artificial intelligence, organizations face escalating challenges in selecting and optimizing Large Language Models (LLMs) for their specific needs. With evolving technologies, fluctuating costs, and varied user requirements, the quest for the "right" model and prompt is no longer a one-time decision. For mid-sized companies, the struggle often lies in limited resources to dynamically evaluate and enhance these models effectively.
The Shift in LLM Dynamics
As LLMs develop, their inherent complexities increase. For example, Anthropic has introduced a new system prompt that further complicates the landscape. Recognizing this challenge, Beekeeper has leveraged Amazon Bedrock to build an innovative system designed to continuously evaluate model and prompt combinations, ranking them in real-time on a dynamic leaderboard. This system intelligently routes requests to the most effective choice based on specific use cases, ensuring optimal outcomes for frontline operations.
Beekeeper: A Digital Workplace Solution
Beekeeper is dedicated to transforming the frontline workforce experience. By providing a mobile-first platform, Beekeeper seamlessly connects non-desk workers to their colleagues and corporate headquarters. Designed to optimize operations in industries like hospitality, manufacturing, retail, healthcare, and transportation, Beekeeper’s solution integrates effortlessly with existing business systems, including HR, scheduling, and payroll.
At its heart, Beekeeper addresses the traditional disconnect between frontline employees and organizational structures, fostering accessible communication and operational efficiency through a robust cloud-based system.
A Dynamic Evaluation System
To tackle the challenge of optimizing LLMs and their prompts, Beekeeper has implemented an automated system meticulously designed to:
- Test varied model-prompt combinations.
- Rank them based on quality, cost, and speed.
- Gather user feedback to refine responses continually.
- Route requests to the most appropriate model.
By scoring quality with synthetic test sets and validating outcomes with user feedback, Beekeeper has created an organically evolving model that strikes a balance between quality, latency, and cost.
Real-World Application: Chat Summarization
A notable feature of Beekeeper is chat summarization for deskless workers. When users return to their shifts, instead of sifting through a multitude of unread messages, they can request a tailored summary highlighting essential action items. This task requires sophisticated technology, including context comprehension, identification of key points, and action item recognition, all while adapting to individual user preferences.
Building the Baseline Leaderboard
Beekeeper’s system begins with establishing a baseline leaderboard. Engineers select various models with domain-specific prompts, testing them against LLM-generated examples to ensure accuracy. This foundational step is crucial for refining future models based on real user feedback.
Evaluation Criteria
The evaluation process leverages both quantitative and qualitative metrics, including:
- Compression Ratio: Evaluates the length of summaries relative to the original text, ensuring clarity and adherence to target lengths.
- Presence of Action Items: Confirms the identification of user-specific action items.
- Lack of Hallucinations: Validates factual accuracy and coherence.
- Vector Comparison: Measures semantic similarity to ideal outputs.
These metrics collectively guide the refinement of prompts and enhance the user experience.
Automating Feedback Incorporation
Once the baseline is established, Beekeeper incorporates user feedback using a structured approach. This feedback, alongside generated outputs, feeds directly into the evaluation process, enabling a continuous loop of refinement.
Additionally, the system employs a prompt mutation process to enhance outcomes based on user input while avoiding significant deviations from established quality standards.
Results and Benefits
By ensuring continual evaluation and adaptation, Beekeeper’s approach offers significant benefits:
- Rapid Evolution: Quickly identifies the most effective model-prompt pairs for user tasks.
- Cost-Efficiency: Balances quality and cost, making LLMs accessible even for smaller engineering teams.
- Personalization: Tailors prompts to unique user needs without compromising other users’ experiences.
Early results indicate a marked 13–24% improvement in response ratings, highlighting the impact of this dynamic evaluation system.
Conclusion
In conclusion, Beekeeper’s automated leaderboard and human feedback system streamline the process of navigating the evolving landscape of LLMs. By continuously optimizing for quality, size, speed, and cost, organizations can leverage the best-performing model-prompt combinations tailored to their specific use cases.
As the AI landscape continues to evolve, Beekeeper aims to expand its capabilities, integrating advanced prompt engineering strategies and allowing users to customize their experiences even further. For organizations exploring LLM optimization, Beekeeper offers a robust methodology to harness the power of AI effectively, facilitating growth and innovation in the digital workplace.
About the Authors:
- Mike (Michał) Koźmiński: Principal Engineer at Beekeeper, focusing on AI integration within products.
- Magdalena Gargas: Solutions Architect at AWS, dedicated to innovation in cloud services.
- Luca Perrozzi: Solutions Architect at AWS, specializing in AI innovation and technology.
- Simone Pomata: Principal Solutions Architect at AWS, committed to advancing customer technology initiatives.