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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
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LLM Prompt OptimizationOptimize your LLM prompts for Retrieval-Augmented Generation (RAG) with the JSON-based, and few-shot techniques. The article explores strategies for reducing hallucinations, improving context alignment, and addressing formatting errors. Extensive testing demonstrates the benefits of structuring prompts with clear instructions, separating context chunks, and leveraging in-context learning examples.
by Luka Panic6 min read
Development
RAG in practice - EmbeddingExplore the embedding process in Retrieval-Augmented Generation (RAG) strategies. This article provides insights into the chunking process, LLM-generated summaries and hypothetical questions, offering valuable analysis on embedding configurations and their impact on answer generation.
by Luka Panic13 min read
Development
RAG in practice - Synthetic Test Set GenerationDive into the practical process of synthetic test set generation with Ragas. This article showcases how AI models are applied to create diverse question types using U.S. Code corpus of data, highlighting real-world testing and cost analysis in RAG applications.
by Luka Panic9 min read
Development
From JavaScript Summer School Student to Full-Time Employee: How and Why That HappenedThis short interview sums up my path from a summer JavaScript School participant to a full time Pixion software engineer explaining how and why that happened.
by Bruno Dapic4 min read
Development
Designing RAG Application: A Case StudyAre you interested in building a Retrieval-Augmented Generation (RAG) application? In previous articles, we introduced fundamental concepts such as retrieval strategies, database choices, vector search indices, and the Ragas evaluation framework. Now let's tie all this together into a comprehensive solution.
by Stipan Petrovic15 min read
Development
Decoding Ragas Using LangSmithExploring the integration of LangSmith, a monitoring platform for AI language systems, with Ragas, a framework for synthetic test set generation and RAG evaluation. LangSmith's detailed tracing capabilities are vital for tracking the dynamic state of a system in production, ensuring seamless performance and insightful analysis.
by Luka Panic13 min read
Development
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