blogs

In this blog, we break down why standard chunking fails for structured data and how to design table-preserving chunking strategies using modern RAG best practices. Each approach comes with implementation guidance, use cases, and architecture fit. Why chunking fails on tabular data? Tables aren’t text — they are relational knowledge graphs compressed into rows and […]

Prompt data types matter more than most developers realize. It started with a simple anomaly. We were building a complex multi-turn conversational agent for a client. The logic was sound, the model was the latest GPT-4, and the context retrieval was optimized. Yet, the agent felt… sluggish. Worse, it was hallucinating during complex reasoning tasks, and

OpenAI’s IndQA benchmark shifts the conversation from translation to native, culture-aware reasoning. Here’s what it means for AI product teams, startups, and enterprises, Published by InteligenAI — 12 November 2025 In today’s age of large-language models (LLMs) and generative AI, one major gap persists: language and cultural depth. Most benchmarks, systems and deployments assume English

November 8, 2025 Open source projects for engineers are transforming development workflows. Discover three open-source projects (opencode, DeepCode, Llama-Factory) that bring AI into developer workflows: inside the terminal, from paper-to-code, and from model-to-deployment. Modern engineering teams want AI that fits their workflow. Whether you’re a backend engineer, a research scientist, or a DevOps lead, developer-native

The AI landscape has shifted dramatically in just a few years. What began with simple chatbots has now evolved into sophisticated AI agents capable of reasoning, planning, and collaborating in real-time. By 2027, 86% of organizations are expected to deploy AI agents, with many leaders projecting triple-digit ROI from these systems. But the real challenge