InfoQ Homepage Programming Content on InfoQ
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/articles/reducing-false-positives-retrieval-augmented-generation/en/smallimage/reducing-false-positives-retrieval-augmented-generation-thumbnail-1762786576150.jpg)
Reducing False Positives in Retrieval-Augmented Generation (RAG) Semantic Caching: a Banking Case Study
In this article, author Elakkiya Daivam discusses why Retrieval Augmented Generation (RAG) and semantic caching techniques are powerful levers for reducing false positives in AI powered applications. She shares the insights from a production-grade evaluation with 1,000 query variations tested across seven bi-encoder models.
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/articles/training-data-preprocessing-for-text-to-video-models/en/smallimage/training-data-preprocessing-for-text-to-video-models-thumbnail-1762244675850.jpg)
Training Data Preprocessing for Text-to-Video Models
In this article, author Aleksandr Rezanov discusses the data preparation for generative text-to-image models to accelerate work on video generation services to be used in TV series and films. He explains how data is prepared and can serve as a starting point for creating custom datasets to develop proprietary models.
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/articles/rag-with-spring-mongo-open-ai/en/smallimage/rag-with-spring-mongo-open-ai-thumbnail-1761292137478.jpg)
Building a RAG Application with Spring Boot, Spring AI, MongoDB Atlas Vector Search, and OpenAI
The RAG paradigm redefines AI: it combines generative models and business data for accurate, contextualised responses. The article shows how to integrate Spring Boot, Spring AI, MongoDB Atlas and OpenAI into a powerful and flexible pipeline capable of transforming the way businesses access and create value from data, with applications ranging from finance and healthcare to customer service.
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/articles/PDCA-AI-code-generation/en/smallimage/AI-code-gen-with-a-formal-and-repeatable-PDCA-cycle-thumb-image-1760692414560.jpg)
A Plan-Do-Check-Act Framework for AI Code Generation
AI code generation tools promise faster development but often create quality issues, integration problems, and delivery delays. A structured Plan-Do-Check-Act cycle can maintain code quality while leveraging AI capabilities. Through working agreements, structured prompts, and continuous retrospection, it asserts accountability over code while guiding AI to produce tested, maintainable software.
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/articles/go-channels-happens-before-concurrency/en/smallimage/Go-Channels-Understanding-Happens-Before-for-Safe-Concurrency-thumb-1759993668874.jpg)
Go Channels: Understanding Happens-Before for Safe Concurrency
This article dives into the happens-before semantics of Go channels, explaining how they relate to memory visibility, synchronization, and concurrency correctness. We'll examine subtle pitfalls, illustrate them with examples, and explore the architectural implications for system designers.
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/articles/unintended-consequences-automation-software/en/smallimage/unintended-consequences-automation-software-thumbnail-1759912534899.jpg)
Exploring the Unintended Consequences of Automation in Software
This article lays out some of the common assumptions and misconceptions about automation and its role in software (and software incidents), what our research has found regarding how automation shows up in software incidents, and some ideas around how people can better design automated tools to help people better handle software incidents.
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/articles/onnx-ai-inference-with-java/en/smallimage/Bringing-AI-Inference-to-Java-with-ONNX-thumb-image-1759229686552.jpg)
Bringing AI Inference to Java with ONNX: a Practical Guide for Enterprise Architects
Java applications can now run transformer-based AI models directly within the JVM—without Python, REST wrappers, or microservices. This guide shows how to integrate ONNX-powered inference with tokenizer support, GPU acceleration, modular deployment, and observability, enabling architects in regulated domains to adopt AI without disrupting compliance or CI/CD workflows.
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/articles/pipeline-language-migrations/en/smallimage/a-pipeline-approach-to-language-migrations-thumb-image-1759158012773.jpg)
A Pipeline Approach to Language Migrations
Automated language migrations can be made reliable and maintainable by structuring them as pipelines with clear, testable stages. This avoids the pitfalls of big-bang rewrites while providing transparency and modularity. The pipeline approach ensures idiomatic code, preserves legacy business logic, and supports large-scale transformations from outdated systems.
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/articles/llms-evolution-ai-infrastructure/en/smallimage/llms-evolution-ai-infrastructure-thumbnail-1758096401785.jpg)
Disaggregation in Large Language Models: the Next Evolution in AI Infrastructure
Large Language Model (LLM) inference faces a fundamental challenge: the same hardware that excels at processing input prompts struggles with generating responses, and vice versa. Disaggregated serving architectures solve this by separating these distinct computational phases, delivering throughput improvements and better resource utilization while reducing costs.
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/articles/ai-ml-data-engineering-trends-2025/en/smallimage/the-infoq-trends-report-500x500-logo-1758095546194.jpeg)
InfoQ AI, ML and Data Engineering Trends Report - 2025
This InfoQ Trends Report offers readers a comprehensive overview of emerging trends and technologies in the areas of AI, ML, and Data Engineering. This report summarizes the InfoQ editorial team’s and external guests' view on the current trends in AI and ML technologies and what to look out for in the next 12 months.
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/articles/timeseries-db-rust/en/smallimage/time-series-database-using-open-source-rebuilding-InfluxDB-in-apache-arrow-rust-thumb-1757058446201.jpg)
Engineering a Time Series Database Using Open Source: Rebuilding InfluxDB 3 in Apache Arrow and Rust
At times, to evolve your product, you need to rebuild it from scratch. The article provides the story behind the rewrite of InfluxDB from scratch using a different programming language - Rust - and stack - Apache Flight, Data Fusion, Apache Arrow and Parquet (FDAP). It emphasises the benefits, as well as the mechanics behind its operation and the different versions of the product.
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/articles/software-engineers-excel-AI/en/smallimage/software-engineers-excel-AI-thumbnail-1756904662117.jpg)
Virtual Panel: How Software Engineers and Team Leaders Can Excel with Artificial Intelligence
Artificial intelligence is impacting the individual work of software developers, how professionals work together in teams, and how software teams are being managed. In this panel, we'll discuss how artificial intelligence is reshaping software development, and what mindset and skills are required for software developers and engineering leaders to become adaptable and resilient in the age of AI.