Revolutionizing Automotive Parts Search
Revolutionizing Automotive Parts Search
Revolutionizing Automotive Parts Search - Innovative Leader in Automotive Data Works with Okareo to Fine-Tune Their RAG System
Business Context
The automotive industry faces a significant challenge: the need for fast and accurate service recommendation and parts search capabilities to improve the efficiency of service technicians and advisors. The ability to quickly search across millions of parts specific to each vehicle and brand translates directly into substantial cost savings in every service department and dealership. One of Okareo’s customers, an innovative data company in the automotive space, recognized the need for a next-generation search solution to address this challenge.
The Approach
The company sought to develop a solution using LLMs and an agent oriented network that could deliver:
High Part Search Accuracy: The system needed to provide precise and reliable results to ensure technicians and advisors could quickly find the correct parts.
Low Latency: Speed was critical; the system needed to deliver search results in real-time to keep up with the demands of a busy automotive service environment.
Data Privacy: The system needed to handle sensitive customer and vehicle data in a secure and compliant manner.
Understanding of Formal Domain-Specific Language: The system needed to understand the unique terminology and nuances of the automotive industry, where terms like "air" and "oxygen" have distinct meanings.
Understanding of Informal Jargon and References: The system needed to understand unique, informal language and written shortcuts specific to each automotive brand and even to individual technicians, parts counters, and dealerships
Complex and Diverse Data Sources: The system needed to integrate and process information from a wide range of sources, including repair manuals, parts catalogs, and NHTSA bulletins.
The Challenge
Developing a solution that leverages Large Language Models (LLMs) for information retrieval and analysis presents a multifaceted challenge. The solution must not only be performant, capable of processing and responding to queries quickly and efficiently, but also highly accurate, delivering precise and relevant information.
Several factors contribute to the complexity of this challenge:
The Unpredictable Nature of LLMs: LLMs, while powerful, can exhibit unpredictable behavior. Their responses may vary based on subtle differences in input phrasing or context, making it difficult to guarantee consistent and reliable results.
The Size of the Retrieval Space: The volume of data that the solution needs to navigate and analyze can be immense. This vast retrieval space necessitates sophisticated indexing and search mechanisms to locate relevant information quickly.
Performance Requirements: The solution must meet stringent performance requirements to provide a seamless user experience. Delays in response times can hinder productivity and user satisfaction.
Given these challenges, the customer recognized the need for a robust methodology to establish a baseline level of quality and identify areas for improvement. This methodology would enable the company to systematically evaluate the performance of their LLM-based solution, measure its accuracy, and pinpoint opportunities for enhancement.
Okareo's Solution for Evaluating Retrieval Augmented Generation
The customer collaborated with Okareo to utilize their expertise in evaluating Retrieval Augmented Generation (RAG) systems. Okareo's solution offered a structured method for breaking down RAG components by intent, embedding, retrieval, and summarization. Furthermore, Okareo's capacity to develop use-case-specific taxonomies allowed the company to utilize Okareo's synthetic data generators. This expanded the evaluation scope and ultimately enabled fine-tuning of the RAG system for improved accuracy and performance.
Key Areas Where Okareo Helped
Structure: Okareo provided a framework for decomposing and structuring the development and evaluation process, enabling the customer to move beyond their initial end-to-end approach and achieve significant improvements.
Evaluations: Okareo's evaluation tools allowed the customer to establish baselines, measure system performance, and evaluate the effectiveness of their solution on their own data.
Synthetic Scenarios: Okareo's synthetic data generation capabilities enabled the customer to get started quickly with limited production data, overcoming data privacy restrictions and facilitating the development of new use cases.
Discrete RAG Phase Evaluation: Okareo's tools allowed the customer to evaluate and tune each phase of the RAG pipeline (Intent Detection, Retrieval, and Generation) independently, enabling targeted improvements.
Synthetic Data for Fine-Tuning: Okareo helped the customer fine-tune their models with domain-specific data, improving the performance of embedding, intent classification, and entity extraction models.
System Wins
Okareo's independent, layered approach to RAG development yielded substantial gains for the customer across key metrics:
Performance: Response times decreased from seconds to milliseconds.
Retrieval Accuracy: Improved from over 98% at k=3.
Intent Classification: Accuracy increased from to near-perfect across major topics and requests.
Model Selection: Rapid assessment and comparison of OpenSource (e.g., LLaMa) and proprietary models (e.g., ChatGPT) enabled data-driven decision-making.
Evaluation Coverage: Extensive synthetic use cases provided comprehensive testing, leading to the customer securing multiple new customers.
Conclusion
Okareo's RAG-based solution empowered the customer to develop a next-generation automotive parts search system that delivers exceptional accuracy, speed, and data privacy. By leveraging Okareo's expertise and tools, the customer achieved significant performance gains, optimized their system architecture, and fine-tuned their models for optimal results.
Revolutionizing Automotive Parts Search - Innovative Leader in Automotive Data Works with Okareo to Fine-Tune Their RAG System
Business Context
The automotive industry faces a significant challenge: the need for fast and accurate service recommendation and parts search capabilities to improve the efficiency of service technicians and advisors. The ability to quickly search across millions of parts specific to each vehicle and brand translates directly into substantial cost savings in every service department and dealership. One of Okareo’s customers, an innovative data company in the automotive space, recognized the need for a next-generation search solution to address this challenge.
The Approach
The company sought to develop a solution using LLMs and an agent oriented network that could deliver:
High Part Search Accuracy: The system needed to provide precise and reliable results to ensure technicians and advisors could quickly find the correct parts.
Low Latency: Speed was critical; the system needed to deliver search results in real-time to keep up with the demands of a busy automotive service environment.
Data Privacy: The system needed to handle sensitive customer and vehicle data in a secure and compliant manner.
Understanding of Formal Domain-Specific Language: The system needed to understand the unique terminology and nuances of the automotive industry, where terms like "air" and "oxygen" have distinct meanings.
Understanding of Informal Jargon and References: The system needed to understand unique, informal language and written shortcuts specific to each automotive brand and even to individual technicians, parts counters, and dealerships
Complex and Diverse Data Sources: The system needed to integrate and process information from a wide range of sources, including repair manuals, parts catalogs, and NHTSA bulletins.
The Challenge
Developing a solution that leverages Large Language Models (LLMs) for information retrieval and analysis presents a multifaceted challenge. The solution must not only be performant, capable of processing and responding to queries quickly and efficiently, but also highly accurate, delivering precise and relevant information.
Several factors contribute to the complexity of this challenge:
The Unpredictable Nature of LLMs: LLMs, while powerful, can exhibit unpredictable behavior. Their responses may vary based on subtle differences in input phrasing or context, making it difficult to guarantee consistent and reliable results.
The Size of the Retrieval Space: The volume of data that the solution needs to navigate and analyze can be immense. This vast retrieval space necessitates sophisticated indexing and search mechanisms to locate relevant information quickly.
Performance Requirements: The solution must meet stringent performance requirements to provide a seamless user experience. Delays in response times can hinder productivity and user satisfaction.
Given these challenges, the customer recognized the need for a robust methodology to establish a baseline level of quality and identify areas for improvement. This methodology would enable the company to systematically evaluate the performance of their LLM-based solution, measure its accuracy, and pinpoint opportunities for enhancement.
Okareo's Solution for Evaluating Retrieval Augmented Generation
The customer collaborated with Okareo to utilize their expertise in evaluating Retrieval Augmented Generation (RAG) systems. Okareo's solution offered a structured method for breaking down RAG components by intent, embedding, retrieval, and summarization. Furthermore, Okareo's capacity to develop use-case-specific taxonomies allowed the company to utilize Okareo's synthetic data generators. This expanded the evaluation scope and ultimately enabled fine-tuning of the RAG system for improved accuracy and performance.
Key Areas Where Okareo Helped
Structure: Okareo provided a framework for decomposing and structuring the development and evaluation process, enabling the customer to move beyond their initial end-to-end approach and achieve significant improvements.
Evaluations: Okareo's evaluation tools allowed the customer to establish baselines, measure system performance, and evaluate the effectiveness of their solution on their own data.
Synthetic Scenarios: Okareo's synthetic data generation capabilities enabled the customer to get started quickly with limited production data, overcoming data privacy restrictions and facilitating the development of new use cases.
Discrete RAG Phase Evaluation: Okareo's tools allowed the customer to evaluate and tune each phase of the RAG pipeline (Intent Detection, Retrieval, and Generation) independently, enabling targeted improvements.
Synthetic Data for Fine-Tuning: Okareo helped the customer fine-tune their models with domain-specific data, improving the performance of embedding, intent classification, and entity extraction models.
System Wins
Okareo's independent, layered approach to RAG development yielded substantial gains for the customer across key metrics:
Performance: Response times decreased from seconds to milliseconds.
Retrieval Accuracy: Improved from over 98% at k=3.
Intent Classification: Accuracy increased from to near-perfect across major topics and requests.
Model Selection: Rapid assessment and comparison of OpenSource (e.g., LLaMa) and proprietary models (e.g., ChatGPT) enabled data-driven decision-making.
Evaluation Coverage: Extensive synthetic use cases provided comprehensive testing, leading to the customer securing multiple new customers.
Conclusion
Okareo's RAG-based solution empowered the customer to develop a next-generation automotive parts search system that delivers exceptional accuracy, speed, and data privacy. By leveraging Okareo's expertise and tools, the customer achieved significant performance gains, optimized their system architecture, and fine-tuned their models for optimal results.
Revolutionizing Automotive Parts Search - Innovative Leader in Automotive Data Works with Okareo to Fine-Tune Their RAG System
Business Context
The automotive industry faces a significant challenge: the need for fast and accurate service recommendation and parts search capabilities to improve the efficiency of service technicians and advisors. The ability to quickly search across millions of parts specific to each vehicle and brand translates directly into substantial cost savings in every service department and dealership. One of Okareo’s customers, an innovative data company in the automotive space, recognized the need for a next-generation search solution to address this challenge.
The Approach
The company sought to develop a solution using LLMs and an agent oriented network that could deliver:
High Part Search Accuracy: The system needed to provide precise and reliable results to ensure technicians and advisors could quickly find the correct parts.
Low Latency: Speed was critical; the system needed to deliver search results in real-time to keep up with the demands of a busy automotive service environment.
Data Privacy: The system needed to handle sensitive customer and vehicle data in a secure and compliant manner.
Understanding of Formal Domain-Specific Language: The system needed to understand the unique terminology and nuances of the automotive industry, where terms like "air" and "oxygen" have distinct meanings.
Understanding of Informal Jargon and References: The system needed to understand unique, informal language and written shortcuts specific to each automotive brand and even to individual technicians, parts counters, and dealerships
Complex and Diverse Data Sources: The system needed to integrate and process information from a wide range of sources, including repair manuals, parts catalogs, and NHTSA bulletins.
The Challenge
Developing a solution that leverages Large Language Models (LLMs) for information retrieval and analysis presents a multifaceted challenge. The solution must not only be performant, capable of processing and responding to queries quickly and efficiently, but also highly accurate, delivering precise and relevant information.
Several factors contribute to the complexity of this challenge:
The Unpredictable Nature of LLMs: LLMs, while powerful, can exhibit unpredictable behavior. Their responses may vary based on subtle differences in input phrasing or context, making it difficult to guarantee consistent and reliable results.
The Size of the Retrieval Space: The volume of data that the solution needs to navigate and analyze can be immense. This vast retrieval space necessitates sophisticated indexing and search mechanisms to locate relevant information quickly.
Performance Requirements: The solution must meet stringent performance requirements to provide a seamless user experience. Delays in response times can hinder productivity and user satisfaction.
Given these challenges, the customer recognized the need for a robust methodology to establish a baseline level of quality and identify areas for improvement. This methodology would enable the company to systematically evaluate the performance of their LLM-based solution, measure its accuracy, and pinpoint opportunities for enhancement.
Okareo's Solution for Evaluating Retrieval Augmented Generation
The customer collaborated with Okareo to utilize their expertise in evaluating Retrieval Augmented Generation (RAG) systems. Okareo's solution offered a structured method for breaking down RAG components by intent, embedding, retrieval, and summarization. Furthermore, Okareo's capacity to develop use-case-specific taxonomies allowed the company to utilize Okareo's synthetic data generators. This expanded the evaluation scope and ultimately enabled fine-tuning of the RAG system for improved accuracy and performance.
Key Areas Where Okareo Helped
Structure: Okareo provided a framework for decomposing and structuring the development and evaluation process, enabling the customer to move beyond their initial end-to-end approach and achieve significant improvements.
Evaluations: Okareo's evaluation tools allowed the customer to establish baselines, measure system performance, and evaluate the effectiveness of their solution on their own data.
Synthetic Scenarios: Okareo's synthetic data generation capabilities enabled the customer to get started quickly with limited production data, overcoming data privacy restrictions and facilitating the development of new use cases.
Discrete RAG Phase Evaluation: Okareo's tools allowed the customer to evaluate and tune each phase of the RAG pipeline (Intent Detection, Retrieval, and Generation) independently, enabling targeted improvements.
Synthetic Data for Fine-Tuning: Okareo helped the customer fine-tune their models with domain-specific data, improving the performance of embedding, intent classification, and entity extraction models.
System Wins
Okareo's independent, layered approach to RAG development yielded substantial gains for the customer across key metrics:
Performance: Response times decreased from seconds to milliseconds.
Retrieval Accuracy: Improved from over 98% at k=3.
Intent Classification: Accuracy increased from to near-perfect across major topics and requests.
Model Selection: Rapid assessment and comparison of OpenSource (e.g., LLaMa) and proprietary models (e.g., ChatGPT) enabled data-driven decision-making.
Evaluation Coverage: Extensive synthetic use cases provided comprehensive testing, leading to the customer securing multiple new customers.
Conclusion
Okareo's RAG-based solution empowered the customer to develop a next-generation automotive parts search system that delivers exceptional accuracy, speed, and data privacy. By leveraging Okareo's expertise and tools, the customer achieved significant performance gains, optimized their system architecture, and fine-tuned their models for optimal results.
Revolutionizing Automotive Parts Search - Innovative Leader in Automotive Data Works with Okareo to Fine-Tune Their RAG System
Business Context
The automotive industry faces a significant challenge: the need for fast and accurate service recommendation and parts search capabilities to improve the efficiency of service technicians and advisors. The ability to quickly search across millions of parts specific to each vehicle and brand translates directly into substantial cost savings in every service department and dealership. One of Okareo’s customers, an innovative data company in the automotive space, recognized the need for a next-generation search solution to address this challenge.
The Approach
The company sought to develop a solution using LLMs and an agent oriented network that could deliver:
High Part Search Accuracy: The system needed to provide precise and reliable results to ensure technicians and advisors could quickly find the correct parts.
Low Latency: Speed was critical; the system needed to deliver search results in real-time to keep up with the demands of a busy automotive service environment.
Data Privacy: The system needed to handle sensitive customer and vehicle data in a secure and compliant manner.
Understanding of Formal Domain-Specific Language: The system needed to understand the unique terminology and nuances of the automotive industry, where terms like "air" and "oxygen" have distinct meanings.
Understanding of Informal Jargon and References: The system needed to understand unique, informal language and written shortcuts specific to each automotive brand and even to individual technicians, parts counters, and dealerships
Complex and Diverse Data Sources: The system needed to integrate and process information from a wide range of sources, including repair manuals, parts catalogs, and NHTSA bulletins.
The Challenge
Developing a solution that leverages Large Language Models (LLMs) for information retrieval and analysis presents a multifaceted challenge. The solution must not only be performant, capable of processing and responding to queries quickly and efficiently, but also highly accurate, delivering precise and relevant information.
Several factors contribute to the complexity of this challenge:
The Unpredictable Nature of LLMs: LLMs, while powerful, can exhibit unpredictable behavior. Their responses may vary based on subtle differences in input phrasing or context, making it difficult to guarantee consistent and reliable results.
The Size of the Retrieval Space: The volume of data that the solution needs to navigate and analyze can be immense. This vast retrieval space necessitates sophisticated indexing and search mechanisms to locate relevant information quickly.
Performance Requirements: The solution must meet stringent performance requirements to provide a seamless user experience. Delays in response times can hinder productivity and user satisfaction.
Given these challenges, the customer recognized the need for a robust methodology to establish a baseline level of quality and identify areas for improvement. This methodology would enable the company to systematically evaluate the performance of their LLM-based solution, measure its accuracy, and pinpoint opportunities for enhancement.
Okareo's Solution for Evaluating Retrieval Augmented Generation
The customer collaborated with Okareo to utilize their expertise in evaluating Retrieval Augmented Generation (RAG) systems. Okareo's solution offered a structured method for breaking down RAG components by intent, embedding, retrieval, and summarization. Furthermore, Okareo's capacity to develop use-case-specific taxonomies allowed the company to utilize Okareo's synthetic data generators. This expanded the evaluation scope and ultimately enabled fine-tuning of the RAG system for improved accuracy and performance.
Key Areas Where Okareo Helped
Structure: Okareo provided a framework for decomposing and structuring the development and evaluation process, enabling the customer to move beyond their initial end-to-end approach and achieve significant improvements.
Evaluations: Okareo's evaluation tools allowed the customer to establish baselines, measure system performance, and evaluate the effectiveness of their solution on their own data.
Synthetic Scenarios: Okareo's synthetic data generation capabilities enabled the customer to get started quickly with limited production data, overcoming data privacy restrictions and facilitating the development of new use cases.
Discrete RAG Phase Evaluation: Okareo's tools allowed the customer to evaluate and tune each phase of the RAG pipeline (Intent Detection, Retrieval, and Generation) independently, enabling targeted improvements.
Synthetic Data for Fine-Tuning: Okareo helped the customer fine-tune their models with domain-specific data, improving the performance of embedding, intent classification, and entity extraction models.
System Wins
Okareo's independent, layered approach to RAG development yielded substantial gains for the customer across key metrics:
Performance: Response times decreased from seconds to milliseconds.
Retrieval Accuracy: Improved from over 98% at k=3.
Intent Classification: Accuracy increased from to near-perfect across major topics and requests.
Model Selection: Rapid assessment and comparison of OpenSource (e.g., LLaMa) and proprietary models (e.g., ChatGPT) enabled data-driven decision-making.
Evaluation Coverage: Extensive synthetic use cases provided comprehensive testing, leading to the customer securing multiple new customers.
Conclusion
Okareo's RAG-based solution empowered the customer to develop a next-generation automotive parts search system that delivers exceptional accuracy, speed, and data privacy. By leveraging Okareo's expertise and tools, the customer achieved significant performance gains, optimized their system architecture, and fine-tuned their models for optimal results.
