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Hellixia Course
New 3-Day Course: Harnessing the Power of Generative AI with Hellixia
This intensive three-day course is designed to help you unlock the full potential of Generative AI in the context of Bayesian networks using Hellixia, BayesiaLab's intelligent GenAI assistant.
Hellixia offers a powerful set of capabilities to streamline the design, analysis, and documentation of knowledge models. The course focuses on the four core functions of Hellixia, giving you practical skills to integrate them into your modeling workflows.
✦ All five functions below can operate either by using only the LLM's internal knowledge or by combining it with specific knowledge files, in a Retrieval-Augmented Generation (RAG) fashion.
1. LLM Knowledge Mining
Automatically generate a wide variety of network types—including Semantic Networks, Semantic Flowcharts, Causal Semantic Diagrams, Knowledge Graphs, and (Risk) Causal Networks—by tapping into the vast knowledge embedded in Large Language Models (LLMs).
2. LLM-Augmented Machine Learning
Identify causal relationships between nodes, with arcs either proposed as Structural Priors or automatically added to the network. These relationships are explained and described by Hellixia to support understanding and validation. Hellixia also automatically generates names for latent variables (clusters of manifest variables) and segments created via data clustering.
3. LLM-Powered Brainstorming Assistants
Support and accelerate brainstorming sessions by leveraging SME-provided dimensions (via BEKEE). Hellixia uses these inputs to construct a semantic network that organizes, clusters, and defines the dimensions—becoming a crucial tool for deciding which dimensions to include in the model. In the quantitative phase, Hellixia assists in the elicitation of Root Priors and ICI Local Effects, providing probability values, confidence levels, and explanatory text.
4. LLM Text-Driven Causal Discovery
Analyze unstructured textual data—such as customer reviews, transcripts, survey responses, or knowledge documents—to extract structured insights using LLMs. Hellixia identifies key drivers and themes, organizes them into a semantic network, and elicits Root Priors and ICI Local Effects for each dimension—enabling full driver analysis and optimization. This mirrors the SME-based brainstorming workflow but relies entirely on LLM intelligence, making it ideal when expert input is unavailable or when handling large volumes of freeform text.
5. LLM-Enhanced Network Documentation Tools
Simplify and enrich the documentation and presentation of networks. Hellixia generates node comments, long descriptive names, and verbalizations of relationships, supports multilingual translation of networks, and creates node-associated images based on their semantic content.
✦ Participants who do not have access to a BayesiaLab license will be provided with a BayesiaLab Token for the Professional Edition valid for 2 months. Additionally, all trainees will receive a 2-month AI Power Pack, providing API keys necessary for Hellixia to access Large Language Models (including OpenAI, Anthropic, Google, Mistral, and Deepseek).