Definitions and Concepts:
- AI is an umbrella term including classic machine learning and Generative AI, which generates new content.
- Large Language Models (LLMs) like ChatGPT act as advanced autocomplete systems trained on vast datasets.
- Prompt engineering is essential for obtaining accurate and relevant responses from LLMs.
- Data types: unstructured (text/documents), semi-structured (CSV, Excel), and structured (SQL databases).
- Two Gen AI approaches:
- Retrieval Augmented Generation (RAG): supplements queries with relevant external documents for more accurate answers in real-time.
- Fine-tuning: custom-trains an LLM on specific data but can be costly and infrequent.

Gen AI on Semi-Structured Data (ChatGPT Use Case):
- ChatGPT can upload and interpret files up to 50MB, such as CSVs, for viewing, querying, and cleaning data.
- Demonstrations include filtering movies by rating, transforming data categories, and enriching data by adding actor information via web lookups.
- Accuracy has improved significantly with version 4.0, enabling handling of hundreds of rows.
- Deep research mode offers enhanced accuracy but takes longer (5-10 minutes).
- Alternatives like AskOnData.com provide advanced data querying but may require payment.

Microsoft Fabric on Structured Data:
- Microsoft Fabric is a comprehensive platform combining ETL, data warehousing, streaming, Spark notebooks, and Power BI.
- Introduces "Data Agents" (formerly AI Skills) to enable natural language querying of structured data.
- Users create and configure data agents by selecting relevant tables and providing hints for accuracy.
- Data agents convert user English queries into SQL queries executed on selected data, supporting role-based security.
- Users can refine queries by providing corrected SQL or hints to improve response accuracy.
- Published data agents can be accessed via URLs in notebooks or other applications, including Microsoft Teams.
- Fabric’s Copilot feature allows interactive chat with data models and is designed for non-technical end users.

Integration and Future Developments:
- Combining unstructured, semi-structured, and structured data via vector databases optimizes querying by retrieving relevant documents before querying LLMs.
- Vector databases filter documents for relevance, which with data agents leads to comprehensive answers.
- Upcoming enhancements in Fabric aim to incorporate semi-structured and unstructured data for consolidated querying.
- Competitors like Amazon and Google are expected to release similar solutions.

Industry Use Cases:
- Healthcare: Integrating patient records, clinical notes, and transcription to enhance diagnosis and treatment with LLMs.
- Personalized Marketing: Combining structured purchase history with social media and product reviews for targeted campaigns.
- Fraud Detection: Leveraging transaction data, call transcripts, legal documents, and emails to identify fraudulent activities.
- Sports Analytics: Analyzing player stats, coaching notes, and game logs for strategy optimization in real-time or pre-match.

Actionable Items:
- Attendees can request a free PDF copy of James Serra’s book by emailing him and mentioning attendance.
- Explore creating data agents in Microsoft Fabric to enable natural language querying on selected structured data.
- Consider integrating vector databases with LLMs for multi-format data querying.
- Experiment with ChatGPT for semi-structured data cleaning and enrichment tasks.
- Keep watch for evolving capabilities in Fabric and competing AI data platforms.