InfoQ Homepage Large language models Content on InfoQ
-
/articles/architectural-intelligence/en/smallimage/architectural-intelligence-small-1732294547540.jpg)
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.
-
/articles/efficient-resource-management-small-language-models/en/smallimage/efficient-resource-management-logo-small-1730888361067.jpg)
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.
-
/articles/llm-adoption-considerations/en/smallimage/virtual-panel-small-logo-1727098889834.jpg)
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.
-
/articles/navigating-llm-deployment/en/smallimage/Navigating-LLM-Deployment-small-1726129303320.jpg)
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.
-
/articles/practical-applications-generative-ai-series/en/smallimage/logo-image-series-small-1726067147994.jpg)
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.
-
/articles/llama3-deployment-applications/en/smallimage/Llama-3-in-Action-tingyi-li-small-logo-1725894826762.jpg)
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.
-
/articles/ai-ml-data-engineering-trends-2024/en/smallimage/InfoQ-Trend-Report-logo-image-small-1725350877013.jpg)
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.
-
/articles/ai-revolution-not-monopolized/en/smallimage/TheAIRevolutionwillnotbeMonopolized-small-1719580808343.jpg)
The AI Revolution Will Not Be Monopolized
Large language models have significantly transformed the field of artificial intelligence. The fundamental innovation behind this change is surprisingly straightforward: make the models a lot bigger. With each new iteration, the capabilities of these models expand, prompting a critical question: are we moving toward a black box era where AI is controlled by a few tech monopolies?
-
/articles/llm-productivity-experiment/en/smallimage/GettyImages-1081869168-small-1715933929540.jpg)
Experimenting with LLMs for Developer Productivity
This article describes an experiment that sought to determine if no-cost LLM-based code generation tools can improve developer productivity. The experiment evaluated several LLMs by generating unit tests for some open-source code and measuring the code coverage as well as the manual rework necessary to make the tests work.
-
/articles/architecture-trends-2024/en/smallimage/the-infoq-trends-report-small-logo-1711613472328.jpeg)
InfoQ Software Architecture and Design Trends Report - April 2024
The InfoQ Trends Reports offer InfoQ readers a comprehensive overview of key topics worthy of attention. The reports also guide the InfoQ editorial team towards cutting-edge technologies in our reporting. In conjunction with the report and trends graph, our accompanying podcast features insightful discussions among the editors digging deeper into some of the trends.
-
/articles/large-language-models-llms-prompting/en/smallimage/Maximizing-the-utility-of-large-language-models-through-prompting-small-1705424512331.jpg)
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.
-
/articles/ai-ml-data-engineering-trends-2023/en/smallimage/the-infoq-trends-report-small-logo-1693560017882.jpeg)
InfoQ AI, ML, and Data Engineering Trends Report - September 2023
In this annual report, the InfoQ editors discuss the current state of AI, ML, and data engineering and what emerging trends you as a software engineer, architect, or data scientist should watch. We curate our discussions into a technology adoption curve with supporting commentary to help you understand how things are evolving.