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Hallucinating Reality. An Essay on Business Benefits of Accurate LLMs and LLM Hallucination Reduction Methods
/in Generative AI /by Robert BoguckiMeta description: Explore the business benefits of accurate LLMs and practical methods to reduce hallucinations. Learn strategies like RAG, CoT, and HITL for AI accuracy today.
Does Your Model Hallucinate? Tips and Tricks on How to Measure and Reduce Hallucinations in LLMs
/in Generative AI /by Katarzyna RutkowskaLearn to measure and reduce LLM hallucinations with expert tips on prompt engineering, RAG, and filters for more accurate, reliable AI responses.
Evaluations, Limitations, and the Future of Web Agents – WebGPT, WebVoyager, Agent-E
/in Generative AI /by Maks Operlejn and Alicja KotylaWeb Agents are no longer just a concept from science fiction—they’re the cutting-edge tools that are automating and streamlining our online interactions at an unprecedented scale. From effortlessly sifting through vast amounts of information to performing complex tasks like form submissions and website navigation, these agents are redefining efficiency in the digital age. Thanks to […]
Why and How to Build AI Agents for LLM Applications
/in Generative AI /by Alan Konarski and Sebastian ChwilczyńskiGenerative AI and Large Language Models (LLMs) have burst onto the scene, introducing us to “copilots,” “chatbots,” and the increasingly pivotal “AI agents.” These advancements unfold at breakneck speed, making it challenging to keep up. We’ve been at the forefront of this revolution, witnessing how AI agents—or “agentic workflows,” as Andrew Ng refers to them—are […]
AI Copilot’s Impact on Productivity in Revolutionizing Ada Language Development
/in Data science, Machine learning /by deepsense.aiHow can we improve and transform developers’ work with a code completion tool? With AdaCore, we wanted to change that and create a proof-of-concept to show the difference.
Optimizing Computational Resources for Machine Learning and Data Science Projects: A Practical Approach
/in Data science, Machine learning /by Łukasz GębalaEvery computation requires computing resources. Sure, sometimes a regular calculator, a piece of paper, and a pencil are sufficient. However, in machine learning, powerful computing resources are necessary.
Implementing Small Language Models (SLMs) with RAG on Embedded Devices Leading to Cost Reduction, Data Privacy, and Offline Use
/in Generative AI /by Kamil CzerskiDiscover how deepsense.ai is leveraging Advanced Retrieval-Augmented Generation (RAG) and Small Language Models (SLMs) to enhance embedded devices, leading to cost savings, improved data privacy, and seamless offline capabilities in the dynamic world of generative AI. Unlock the potential of cutting-edge technology for your business success.
From LLMs to RAG. Elevating Chatbot Performance. What is the Retrieval-Augmented Generation System and How to Implement It Correctly?
/in Generative AI /by Patryk KowalskiDiscover how Retrieval-Augmented Generation (RAG) systems outperform Large Language Models (LLMs) in enhancing chatbot performance for businesses. Learn what RAG is and gain insights into correct implementation strategies.
Reducing the cost of LLMs with quantization and efficient fine-tuning: how can businesses benefit from Generative AI with limited hardware?
/in Generative AI /by Alicja Kotyla and Artur ZygadloExplore how businesses can utilize generative AI efficiently, even on limited hardware. Learn cost-effective strategies for LLMs. Start reading now!
Achieving accurate image segmentation with limited data: strategies and techniques
/in Artificial Intelligence /by Sebastian ChwilczyńskiLearn how to perfect image segmentation with limited data. Unlock tested strategies and master new techniques. Read our article now!