Overview and Objectives Building on this foundation, we frame why a production-ready evaluation system matters for modern LLM development: experiments without rigorous tracking are noisy, non-reproducible, and hard to scale. Native LLM Evaluation needs to live where your data and compute already are, so you can compare model outputs against

Map funnel steps and events Building on this foundation, start by treating the conversion funnel as a precise map of user intent: identify the discrete funnel steps you care about, then agree on the exact events that signal each step. Conversion funnel and funnel steps should appear immediately in your

Core SQL Concepts Building on this foundation, mastering a few fundamental relational concepts will get you through most SQL interview questions and make you a stronger data engineer. Start by treating the relational model as the contract between your data and queries: tables represent entities, columns represent attributes, and foreign

Agentic AI: Definition and Overview Agentic AI transforms language models from passive answer generators into goal-driven systems that take actions in the world. In practice, Agentic AI means composing an autonomous LLM with planning, state, and tool access so the model can pursue objectives over multiple steps rather than only

Definitions: Conversational vs Agentic AI Building on this foundation, we need to be precise about two often-confused categories: Conversational AI and Agentic AI. Conversational AI refers to systems optimized for dialogue—understanding intent, maintaining context, and producing responses that keep a human engaged. Agentic AI refers to systems that take autonomous,

PyData stack: tools and ecosystem overview When you assemble a modern Python-based data platform, you’re really choosing an ecosystem of interoperable tools rather than a single monolithic framework. The PyData stack centers on NumPy for numerical arrays and pandas for tabular data manipulation; these two libraries define most in-memory data

Clarify business objectives Building on this foundation, the first practical step is turning broad ambitions into concrete, measurable targets that link AI work to business objectives, ROI, and operational performance. If you want your AI project to move from a research proof-of-concept to production, you must define what success looks

Business model and value proposition ChatGPT and prompt engineering were the engines we used to convert a technical capability into a predictable commercial engine. In the first product sprint we focused on building AI-driven services that solved a concrete pain: teams needed fast, high-quality content, code scaffolding, and data enrichment

Correlation vs Causation Defined Building on the foundation we established earlier, the difference between correlation and causation is the single most important distinction you’ll make when moving from descriptive analytics to causal inference. Correlation describes a statistical association: two variables move together more often than not, which you can quantify