On this tutorial, we’ll discover implement the LLM Area-as-a-Choose method to judge massive language mannequin outputs. As a substitute of assigning remoted numerical scores to every response, this methodology performs head-to-head comparisons between outputs to find out which one is best — primarily based on standards you outline, equivalent to helpfulness, readability, or tone. Try…
In this tutorial, we dive deep into the advanced capabilities of OpenBB to perform comprehensive portfolio analysis and market intelligence. We start by constructing a tech-focused portfolio, fetching historical market data, and computing key performance metrics. We then explore advanced technical indicators, sector-level performance, market sentiment, and correlation-based risk analysis. Along the way, we integrate…
Mirascope is a powerful and user-friendly library that provides a unified interface for working with a wide range of Large Language Model (LLM) providers, including OpenAI, Anthropic, Mistral, Google (Gemini and Vertex AI), Groq, Cohere, LiteLLM, Azure AI, and Amazon Bedrock. It simplifies everything from text generation and structured data extraction to building complex AI-powered…
In this tutorial, we explore how to build an intelligent and self-correcting question-answering system using the DSPy framework, integrated with Google’s Gemini 1.5 Flash model. We begin by defining structured Signatures that clearly outline input-output behavior, which DSPy uses as its foundation for building reliable pipelines. With DSPy’s declarative programming approach, we construct composable modules,…
In this tutorial, we explore how to leverage the PyBEL ecosystem to construct and analyze rich biological knowledge graphs directly within Google Colab. We begin by installing all necessary packages, including PyBEL, NetworkX, Matplotlib, Seaborn, and Pandas. We then demonstrate how to define proteins, processes, and modifications using the PyBEL DSL. From there, we guide…
Upstage’s Groundedness Check service provides a powerful API for verifying that AI-generated responses are firmly anchored in reliable source material. By submitting context–answer pairs to the Upstage endpoint, we can instantly determine whether the supplied context supports a given answer and receive a confidence assessment of that grounding. In this tutorial, we demonstrate how to…
In this tutorial, we introduce TinyDev class implementation, a minimal yet powerful AI code generation tool that utilizes the Gemini API to transform simple app ideas into comprehensive, structured applications. Designed to run effortlessly in Notebook, TinyDev follows a clean three-phase workflow—Plan → Files → Code—to ensure consistency, functionality, and modular design. Whether building a…