Bridging Faculty Education and AI Research & Development: Luka’s Story Discover Luka's story, a CS student and AI researcher at Pixion, about contributing to cutting-edge generative AI projects, open-source innovations, and impactful R&D insights that have been recognised across the AI community.
by Luka Panic3 min read
Our People
Code, Coffee, and Growth: Gordan’s Life as a Pixion Software EngineerDiscover Gordan’s journey as a software engineer at Pixion since 2021. Explore his experience with challenging projects, a supportive team culture, and professional growth opportunities. Learn about his tech stack, from React and SQL to Docker, plus his hobbies like Magic: The Gathering and baking.
by Gordan Graberec4 min read
Our People
Vector Database Benchmark - OverviewVector databases are crucial for AI-driven applications, but selecting the best one can be tricky. This article breaks down key performance metrics such as recall, nDCG, and QPS, focusing on the HNSW algorithm and VectorDBBench for benchmarking. It also features Docker Compose setups for Milvus, Redis, Chroma, and PgVector to ensure fair and consistent performance testing.
by Luka Panic8 min read
Development
A Hot Potato’s Journey to Well Cooked ProductsAs a small but mighty agency, we pride ourselves on our ability to innovate and adapt in the always evolving world of Software Development and Design. At Pixion, our secret weapon for constantly improving our workflow is teamwork and as a Product Designer, one of my favorite methods for working closely with our Engineers to create beautiful and reliable products is the Hot Potato Process.
If you haven't heard of it before, let me introduce you to the approach that helps keep our team ahead of the game.
by Katie Tavares6 min read
Design
RAG in practice - EvaluationExplore the RAG evaluation on a large scale to assess the effectiveness of different strategies. This article analyzes answer quality across various configurations, addressing challenges like answer coverage, comparison methods, and rate limits. It also provides insights into the cost and resource impact of running the evaluation pipeline.
by Luka Panic15 min read
Development
RAG in practice - Answer GenerationDiscover how various parameters affect performance in Retrieval-Augmented Generation (RAG). This article explores factors like chunk size, overlap, and context size, examining their impact on the large language model's ability to answer questions. It also delves into error analysis and the cost breakdown of running the full pipeline, including token usage and pricing considerations.
by Luka Panic25 min read
Development