All Blog Posts

LLMs do not forget because they are broken. They forget because memory is hard. Long context windows sound impressive, but real intelligence is not about storing everything forever. It is about compression, relevance, hierarchy, and timing. This article breaks down why long context is not long-term understanding, why forgetting is often a feature rather than a bug, and how true AI systems will evolve toward structured memory and reflection.

Retrieval-Augmented Generation (RAG) was never meant to stop at “retrieve and prepend.” Classic RAG helped LLMs access external knowledge, but real-world problems forced it to evolve. This article explains, in simple but technical language, how RAG is moving from basic retrieval into Modular RAG, Agentic RAG, and Graph-Enhanced RAG ,transforming RAG from a lookup tool into an adaptive external memory and reasoning engine.

Modern AI models sometimes act like “Yes-Men,” agreeing with false statements instead of correcting them. This happens because Large Language Models aren’t truth engines , they’re pattern completers. This article explains in simple language how model design creates sycophancy, and why **Context Engineering** is the key to fixing it.

Prompt engineering feels powerful until it suddenly breaks. As modern AI systems grow more complex, clever prompts are no longer enough. The real leverage has shifted from wording prompts to designing the context that surrounds a model. This article explains why prompt engineering hit its limits and how context engineering replaces it.

AI models gain surprising new abilities as they scale, but even the most advanced systems lack the core ingredients of consciousness. While larger models show emergent reasoning and complex behavior.

AI is rapidly transforming the world of software development, leading many to wonder if human developers will become obsolete. But while AI handles repetitive coding tasks, true innovation still depends on human creativity and problem-solving ,making developers more important than ever.