InfoQ Homepage Generative AI Content on InfoQ
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Elevate Developer Experience with Generative AI Capabilities on AWS
This is a summary of a talk I gave at InfoQ Dev Summit Munich 2024. I discussed the transformative potential of generative AI in enhancing developer experiences, particularly through AWS. I’ll introduce key tools like Amazon Bedrock, Code Review Assistant, Agentic Code Generation, and Code Summarization in this article.
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A Framework for Building Micro Metrics for LLM System Evaluation
LLM accuracy is a challenging topic to address and is much more multi-dimensional than a simple accuracy score. Denys Linkov introduces a framework for creating micro metrics to evaluate LLM systems, focusing on goal-aligned metrics that improve performance and reliability. By adopting an iterative "crawl, walk, run" methodology, teams can incrementally develop observability.
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Architectural Intelligence – the Next AI
Architectural Intelligence is the ability to look beyond AI hype and identify real AI components. Determining how, where, and when to use AI elements comes down to traditional trade-off analysis. Like any technology, AI can be used creatively, but inappropriately. Identify if AI makes sense for your use case, then work to use it effectively to meet your needs.
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Efficient Resource Management with Small Language Models (SLMs) in Edge Computing
Small Language Models (SLMs) bring AI inference to the edge without overwhelming the resource-constrained devices. In this article, author Suruchi Shah dives into how SLMs can be used in edge computing applications for learning and adapting to patterns in real-time, reducing the computational burden and making edge devices smarter.
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Virtual Panel: What to Consider When Adopting Large Language Models
Four experts discuss some issues people should think about when adopting LLMs and how they can make the best choice for their specific use case. Topics include how to choose between an API-based vs. self-hosted LLM, when to fine-tune an LLM, how to mitigate LLM risks, and what non-technical changes organizations need to make when adopting LLMs.
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Navigating LLM Deployment: Tips, Tricks, and Techniques
This article focuses on self-hosted LLMs and how to get the best performance from them. The author provides best practices on how to overcome challenges due to model size, GPU scarcity, and a rapidly evolving field.
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Article Series: Practical Applications of Generative AI
Generative AI (GenAI) has become a major component of the artificial intelligence (AI) and machine learning (ML) industry. However, using GenAI comes with challenges and risks. In the InfoQ "Practical Applications of Generative AI" article series, we present real-world solutions and hands-on practices from leading GenAI practitioners.
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Llama 3 in Action: Deployment Strategies and Advanced Functionality for Real-World Applications
This article details the enhanced capabilities of the open-source Llama 3 LLM, and how businesses can adopt the model in their applications. The author gives step-by-step instructions for deploying Llama 3 in the cloud or on-premise, and how to leverage fine-tuned versions for specific tasks.
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InfoQ AI, ML and Data Engineering Trends Report - September 2024
InfoQ editorial staff and friends of InfoQ are discussing the current trends in the domain of AI, ML and Data Engineering as part of the process of creating our annual trends report.
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Using Generative AI in Software Project Management to Bridge Domains and Accelerate Productivity
Gen AI Assistants play to the strengths of professionals with a breadth of experience, particularly software developers who can describe what they want the LLM to complete and critically evaluate the result. These tools enable us to swiftly cross divides of domain language and scale large repetitive tasks down to interesting ones on a human scale.
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Generative AI: Shaping a New Future for Fraud Prevention
This article explores how generative AI affects fraud detection by reducing false positives and dynamically adapting to changing fraud patterns. This combination offers a potent preventive solution when integrated with machine learning. The efficacy and scalability of fraud prevention initiatives are enhanced by this innovative approach.
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Maximizing the Utility of Large Language Models (LLMs) through Prompting
In this article, authors Numa Dhamani and Maggie Engler discuss how prompt engineering techniques can help use the large language models (LLMs) more effectively to achieve better results. Prompting techniques discussed include few-shot, chain-of-thought, self-consistency, and tree-of-thoughts prompting.