Staying ahead of the AI game with tailored LLM and RAG Solutions for success
We set out to develop an AI travel assistant and expand on our expertise, but the project led us on a journey to build a scalable solution that is adaptable to broader needs. Along the way, we tackled unique challenges, documented our journey, and shared key insights to support others in navigating similar endeavors.
Facing the challenges of emerging AI technologies
When our client sought our expertise to develop a proof-of-concept AI assistant for tourists, we faced the challenge of navigating the fast-paced, ever-evolving landscape of AI tools. With many technologies too new to offer established reviews or reliable benchmarks, identifying the best fit for the project’s unique requirements demanded a strategic and innovative approach.
Tackling AI development with a strategic top-down approach
To address this challenge, we adopted a top-down approach, designing a versatile Retrieval-Augmented Generation (RAG) data ingestion and retrieval pipeline that could adapt to virtually any business need without being tied to a specific LLM. We also developed an enterprise-ready, scalable cloud solution to support the application's growth and evolving requirements. To contribute to the community, we created a RAG learning tool and made it available on GitHub, enabling developers and students to explore and experiment with RAG strategies. Throughout the process, we documented our journey in detailed blog posts, breaking down the complexities of building an LLM-based application. These resources covered RAG strategies, performance evaluation, and best practices, culminating in a solution that was not only high-performing but also future-proofed for advancements in AI.
- MRKL Autonomus Agents
- RAG-Enhanced Assistants and Bots
- Data Ingestion and Retrieval Pipelines
- LLM Application Evaluation Solutions
- Learn RAG Learning App
.NET Core
Semantic Kernel
Python
LangChain
LangSmith
PostgreSQL
MongoDB
Chroma
ReactAI travel assistant
We were tasked with building a proof-of-concept AI travel assistant for a client in the tourism industry. The goal was to create an intelligent assistant capable of autonomously performing tasks like checking the weather, booking hotels, and finding local events through conversational interactions. As we worked through the project, we focused on creating agentic AI solutions that could act on behalf of the user, providing seamless and personalized experiences.
Enterprise solutions powered by AI
As the project evolved, it became clear that the AI domain was much broader, prompting us to leverage Azure OpenAI Service to meet enterprise requirements. Our AI solutions securely integrate generative AI with your data, ensuring compliance and optimized performance with region-based data residency. We prioritize infrastructure security to safeguard your data at every level. Fully compatible with Microsoft and Azure products, our solutions provide seamless integration and scalability, offering secure, high-performance AI solutions tailored for enterprise needs.
Benchmarking futureproofed RAG solutions
Motivated by the lack of reliable metrics in existing resources, we aimed to create a reusable, general toolset for objectively evaluating various RAG strategies, leading to the development of a complex system designed to test different combinations of RAG strategy parameters, offering flexibility for benchmarking and potential future use cases. With an abundance of combinations, we encountered key challenges: managing execution costs, ensuring clear price communication, and tracking progress throughout the process. To tackle these, we optimized the execution flow, eliminating redundant steps and improving efficiency. We also implemented a system that provides cost insights and an easy way to restart failed processes.
Sharing AI and RAG insights through blog posts
After overcoming technical challenges and optimizing our solution, we benchmarked various configurations and shared our findings in a series of detailed blog posts. These posts provided valuable insights into RAG strategies, offering developers and AI enthusiasts a deeper understanding of performance evaluations, best practices, and the lessons we learned.
We built LLM expertise while boosting awareness of Pixion's capabilities and driving traction towards our blog.
Meet our Learn RAG Application, a tool that allows users to explore and experiment with Retrieval-Augmented Generation (RAG) pipelines in an interactive format. This tool empowers developers and students to understand and manipulate different RAG strategies, fostering a hands-on learning experience. By making this tool available on GitHub, we aim to contribute to the community.
A preview of how different RAG strategies operate
The tool also offers a preview into details of different RAG strategies. Users can experiment with various strategies by modifying parameters, testing different data sources, and observing the effects on the results.
“By embracing the latest trends, Pixion not only solidifies its capability to deliver enterprise-grade solutions but also positions itself as a thought leader in this rapidly evolving domain.”