Discover how Dwarves use inefficiency arbitrage to spot and solve client tech gaps. Learn practical ways to find opportunities and deliver value.

Inefficiency arbitrage

IndexedDB is a low-level API for client-side storage like localStorage and cookies. But this built-in non-relational database is much more powerful than those 2 counterparts.

Intro to IndexedDB

Caching is a vital technique that boosts performance by storing frequently accessed information. Let's see how it works with RAG. In this article, we will know how cache could be implement in RAG system.

Evaluating caching in RAG systems

In baseline Retrieval Augmented Generation (RAG), sometimes the result might not be accurate as expected since the query itself have multiple layers of reasoning or the answer requires traversing disparate pieces of information through their shared attributes in order to provide new synthesized insights. In this post, we will explore a new approach called GraphRAG which combines the strengths of knowledge graphs and large language models to improve the accuracy of RAG systems

GraphRAG - building a knowledge graph for RAG system

Hybrid search comes to solving problems in vector search and full-text search. Let's see how it works. In this article, we will know how hybrid search approach and how to evaluate each search method through metrics in information retrieval.

Evaluating search engine in RAG systems

In spite of having taken the world by storm, Large Language Models(LLM) still has some limitations such as limited context window and a knowledge cutoff date. Retrieval-Augmented Generation(RAG) steps in to bridge this gap by allowing LLMs to access and utilize external knowledge sources beyond their training data. However, data is not text based only, it also can be image, audio, table in docs,...

Multimodal: in rag

Re-ranking is a crucial step in Retrieval-Augmented Generation (RAG) systems that addresses the challenge of retrieving heterogeneous and potentially irrelevant information. By evaluating and re-ordering retrieved documents, re-ranking ensures that only the most relevant and useful information is passed to the generation model. This process significantly improves the coherence, accuracy, and relevance of the generated text, ultimately enhancing the reliability and effectiveness of RAG systems.

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