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AI, ML & Data Engineering

Domain-Driven RAG: Building Accurate Enterprise Knowledge Systems through Distributed Ownership

Retrieval augmented generation (RAG) can help reduce LLM hallucination. Learn how applying high-quality metadata and distributing ownership of documents and prompts to domain experts can further increase accuracy in RAG applications. An additional layer of intelligence can use metadata to focus RAG searches on a specific domain for even better results.

George Panagiotopoulos
on May 06, 2025
AI, ML & Data Engineering

Beyond Chatbots: Architecting Domain-Specific Generative AI for Operational Decision-Making

This article explores the use of domain-specific Generative AI, models that understand operational constraints, real-world dynamics, and business rules to generate executable strategies, not just text descriptions. These models require significantly smaller datasets and fewer parameters, making them cost-effective while enabling AI-driven core business decision intelligence at scale.

Abhishek Goswami
on Apr 02, 2025
AI, ML & Data Engineering

Building Trust in AI: Security and Risks in Highly Regulated Industries

Explore the transformative power of responsible AI across industries, emphasizing security, MLOps, and compliance. As AI drives innovation—from predicting hurricanes to enhancing legal workflows—organizations must prioritize ethical practices, transparency, and robust governance to safeguard sensitive data while navigating an evolving regulatory landscape.

Stefania Chaplin Azhir Mahmood
on Feb 10, 2025
AI, ML & Data Engineering

Launching GenAI Productivity Tools: Insights and Lessons

In this article, based on a talk at QCon San Francisco 2024, author Mandy Gu shares some of the ways her company uses GenAI to enhance productivity and the lessons they learned along the way, including failed bets and features that were rolled back because of low user adoption. Most important, they learned to focus on building tools that were aligned with business goals.

Mandy Gu
on Feb 06, 2025
AI, ML & Data Engineering

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.

Olalekan Elesin
on Jan 27, 2025
AI, ML & Data Engineering

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.

Denys Linkov
on Jan 21, 2025
Architecture & Design

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.

Thomas Betts
on Nov 26, 2024
AI, ML & Data Engineering

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.

Suruchi Shah
on Nov 11, 2024
AI, ML & Data Engineering

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.

Anthony Alford Meryem Arik Numa Dhamani Maggie Engler Tingyi Li
on Oct 01, 2024
AI, ML & Data Engineering

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.

Meryem Arik
on Sep 24, 2024
AI, ML & Data Engineering

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.

Anthony Alford
on Sep 17, 2024
AI, ML & Data Engineering

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.

Tingyi Li
on Sep 17, 2024