Data Analytics

Large language models are remarkable text generators, but they have a well-documented weakness: they hallucinate. Without access to verified, up-to-date information, they confidently produce plausible-sounding answers that are factually wrong. Retrieval-Augmented Generation (RAG) emerged as the standard solution, grounding model outputs in external knowledge sources and dramatically reducing hallucinations across enterprise applications. But as organizations…

The term "agentic AI" has moved from research papers into production infrastructure faster than most people anticipated. Systems that autonomously plan, use tools, and iterate toward goals are now deployed in code review pipelines, research assistants, customer operations platforms, and internal automation tools at companies of every size. But building agents that actually work reliably…

Your data science team is under increasing pressure to develop more sophisticated machine learning algorithms. Meanwhile, the regulatory landscape around enterprise AI is growing more complex and more restrictive. That is a dilemma for your team when you need space to experiment, but also need to adhere to clear governance rules. To reconcile these priorities,…

Newcomers headed for data and AI work tend to treat the basics of Python as a waiting room. The plan is often just to get through them quickly and arrive at the prime time libraries, where the real work is assumed to happen. It is an understandable plan. However, it produces a particular kind of…

Google shipped Gemini 3.5 Transcribe on August 26, 2026, and the timing makes it a genuinely useful comparison. OpenAI had released its own current flagship transcription model, GPT-Transcribe, just four weeks earlier, on July 28, 2026. Two labs, two new transcription models, released close enough together that comparing them actually means something right now instead…

Anthropic released Claude Opus 5.5 on September 22, 2026. It's the first model in the new Claude 5.5 family, and according to Anthropic's own announcement, it performs at roughly the level of Claude Fable 5.1 on most work while costing 40% less to run than Opus 5. This article pulls together every verifiable number and…

In a previous article we discussed constraining output space for small language model (SLM) narrow automation optimization. We mentioned at the time that this was the first in a short series of SLM optimization strategy articles. We are now at the second of those. This time we will focus on the reuse of the prompt…

Introduction Spaghetti code is hard to work with because its logic is tangled. A function in Python can handle several related steps and still be perfectly readable. Problems start when different responsibilities become tightly connected, dependencies are unclear, and changing one piece of logic requires tracing through unrelated parts of the code. Breaking code into…

The Problem With "Just RAG" Retrieval-Augmented Generation changed the game for enterprise AI. Instead of hoping a large language model memorized the right answer during pre-training, RAG grounds responses in your own documents — embed a user's question, find similar chunks in a vector database, and pass them to an LLM as context. It is…

We ran an experiment: three small datasets, one AI model, and the questions a business team asks in a normal week — what's our average delivery time, which region is our best performer, how many athletes are in this file. Then we added a review pass. We handed the model its own answer back and…