Co-founder Athina AI (Y Combinator W23)
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As we build more complex AI applications day by day, there is a need to implement advanced RAG techniques to get better responses from LLMs. We all know RAG is the fundamental piece in building LLM systems. It enables LLMs to generate contextually relevant responses by using external knowledge sources.
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Introduction In our Advanced RAG Techniques series, today we’re exploring a powerful technique to improve traditional RAG called Corrective RAG (CRAG). Since LLMs have the limitation of generating responses based on their pre-trained knowledge, RAG has become a popular technique for making them smarter by connecting them to external
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Introduction AI copilots assist medical practitioners by providing real-time guidance, supporting diagnosis with data-driven insights and personalized treatment recommendations. Medical practitioners need to look at a lot of details about patients before prescribing the medication—like the patient’s medical history, current symptoms, and how new medications might interact with
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In 2023, Retrieval-Augmented Generation (RAG) became one of the most talked-about breakthroughs in AI, transforming how systems handle context and knowledge. The 2024 was the year of AI Agents, which opened up exciting possibilities for building more autonomous and interactive applications. If you’re just entering the world of Generative
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[Why Human Evaluation and Annotation is Important to Build Reliable LLM Systems] Human evaluation is a critical part of building reliable, controlled, and responsible AI. This ensures that your AI models are aligned with human criteria. Similarly, human annotation is important to: * Construct and audit automatic benchmarks * Create training data