markdown updates- intro for construction engineering megalab

dev-ai-app-dev-constructioneng-aiexperience/build/build.md

@@ -4,8 +4,6 @@
 
 In this lab, you build a construction procurement engine with Oracle AI Database and OCI Generative AI. Connect to the database, explore the sample procurement data, and invoke a large language model to generate supplier recommendations and risk explanations. Building on earlier exercises, you’ll apply Python to deliver a fully integrated, AI-powered construction procurement application.
 
-This lab uses some of the basic coding samples you created in lab 3, such as `cursor.execute` and more.
-
 Estimated Time: 30 minutes
 
 ### Objectives
@@ -893,8 +891,6 @@ To summarize, you:
 
 Congratulations, you completed the lab.
 
-You may now proceed to the next lab.
-
 ## Learn More
 
 * [Code with Python](https://www.oracle.com/developer/python-developers/)

dev-ai-app-dev-constructioneng-aiexperience/introduction/introduction.md

@@ -2,60 +2,63 @@
 
 ## About this Workshop
 
-Construction procurement runs on connected decisions. Project requirements, supplier qualifications, compliance documents, schedules, budgets, and risk signals all need to come together quickly if teams want to avoid delays and make confident award decisions.
+Construction procurement depends on connected decisions. Project requirements, supplier qualifications, compliance documents, schedules, budgets, and risk signals all need to come together quickly so teams can avoid delays and make confident supplier decisions.
 
-SeerGroup is a global conglomerate with multiple divisions. Across those divisions, the challenge is the same: **critical decisions take too long** because data is scattered across spreadsheets, documents, forms, and disconnected systems.
+SeerGroup is a global conglomerate with multiple divisions. Across those divisions, the challenge is the same: critical decisions take too long when data is scattered across spreadsheets, documents, forms, and disconnected systems.
 
-This workshop shows how Seer Construction addresses that problem with Oracle AI Database + OCI Generative AI. By keeping procurement data in one place, the team reduces brittle integrations and gives procurement officers, engineers, and managers a single platform for faster reviews, clearer supplier recommendations, and better project outcomes.
+This workshop shows how Seer Construction addresses that problem with Oracle AI Database and OCI Generative AI. By keeping procurement data in one place, the application helps procurement officers, engineers, and managers review projects faster, generate clearer supplier recommendations, and make better-informed decisions.
 
-You’ll see how Seer Construction moves from manual, fragmented procurement workflows to AI-powered applications, and you’ll build the same capabilities yourself in the labs that follow.
+First, you will experience the application from the procurement user’s point of view. Then, you will go behind the scenes and build the same application flow step by step using Oracle AI Database capabilities, including **AI Vector Search**, **Property Graph**, and **JSON Duality Views**; **OCI Generative AI** for AI-generated responses; Python for application logic; and RAG to ground answers in retrieved business context.
 
-Estimated Workshop Time: 90 minutes
+Estimated Workshop Time: 60 minutes
 
-✅ **Start with the demo! (Lab 1)**
 
-Step into Seer Construction’s Construction Engineering division as a construction procurement officer. You’ll use an AI-powered construction procurement app built on Oracle AI Database to:
+✅ **Lab 1 — Experience the app as a procurement user**
 
-* Review project procurements with AI Vector Search, Graph analytics, and RAG
-* See how Generative AI evaluates project requirements, risk, and supplier readiness against available supplier options
-* Approve, deny, or request more information with AI-generated guidance, risk factors, and decision summaries
-* Update procurement profiles using JSON Duality Views so project data stays consistent
+In Lab 1, you will use the **Seer Construction Procurement app** as a construction procurement officer. You will review project details, ask questions through the AI Procurement Guru, approve or deny supplier recommendations, explore project risk relationships, and update a project profile from an uploaded document.
+You will see how the application can:
 
-This story shows how Seer Construction uses Oracle AI Database and OCI Generative AI to accelerate procurement review, reduce supplier risk, and improve decision quality without moving data across fragile systems.
+* Review project procurements with AI-generated analysis and recommendations.
+* Answer follow-up questions using RAG and AI Vector Search.
+* Support approval, denial, or request-for-information decisions with supplier evidence, risk factors, and decision summaries. 
+* Show connected project, supplier, recommendation, and risk data using Property Graph.
+* Update project profile information using JSON Duality Views and JSON Transform.
 
-✅ **What’s next (Labs 2–3)**
+This demo shows how Oracle AI Database and OCI Generative AI can help streamline procurement review, reduce supplier risk, and improve decision quality without moving data across fragile systems.
 
-After the demo, you’ll switch roles to developer. In the next labs you’ll connect to Oracle AI Database, shape data into JSON Duality Views, build a retrieval-augmented application, and extend it with reusable MCP tools. By the end, you’ll see how SeerGroup teams can move from siloed procurement processes to intelligent, AI-driven applications on one platform.
+✅ **Lab 2 — Build the application flow step by step**
 
-* **Lab 2 – Connect to your environment**
-Log in to the JupyterLab IDE where you’ll write Python and run your code. Build the data foundation by using Python with Oracle AI Database to create tables, shape them into JSON Duality Views, and interact with them using both SQL and MongoDB-style syntax. Implement RAG by constructing a working AI application that pulls procurement and supplier data, generates recommendations with OCI Generative AI, chunks and vectorizes the results, and answers follow-up questions with Vector Search + RAG.
+After using the demo application in Lab 1, you will switch from procurement user to developer. In Lab 2, you will work in a **JupyterLab development environment** and build the same application flow step by step.
 
-* **Lab 3 – Extend with MCP tools**
-Wire Oracle AI Database and OCI Generative AI into reusable MCP tools. Call them from notebooks, chain them together into workflows, and register tools that support SeerGroup’s construction, retail, healthcare, or energy teams.
+You will connect to Oracle AI Database from Python, retrieve project data from a JSON relational duality view, use OCI Generative AI to generate supplier recommendations, chunk and store the recommendation text, create vector embeddings, and use AI Vector Search with RAG to answer follow-up questions.
+
+By the end of the workshop, you will have experienced the application as an end user and built the core AI workflow behind it as a developer.
 
-By the end, you’ll have a complete toolkit, from clean procurement data to live AI apps to composable tools, that shows how SeerGroup divisions can turn operational data into intelligent applications.
 
 ### Objectives
 
-* Build and query data with Python + Oracle AI Database
-* Shape relational data into documents using JSON Duality Views
-* Run Vector Search, Graph analytics, and RAG directly in the database
-* Extend apps with OCI Generative AI and MCP tools
-* Deliver industry-grade solutions for SeerGroup’s divisions
+In this workshop, you will:
+
+* Run an AI-powered construction procurement demo application. 
+* Query project and supplier data from Oracle AI Database. 
+* Use JSON Duality Views to work with relational data as project-centered JSON documents. 
+* Generate supplier recommendations with OCI Generative AI. 
+* Use AI Vector Search and RAG to answer follow-up questions with retrieved business context. 
+* Explore connected project and supplier relationships with Property Graph. 
+* Build the core application flow using Python. 
 
 ### Prerequisites
 
-This lab assumes you have:
+This workshop assumes you have:
 
-* An Oracle account to submit your LiveLabs Sandbox reservation
-* Basic knowledge of Python
-* Basic knowledge of Oracle Database, including how to run queries
+* An Oracle account to submit a LiveLabs Sandbox reservation. 
+* Basic knowledge of Python. 
+* Basic knowledge of Oracle Database, including how to run queries.
 
 ## Learn More
 
 * [Oracle AI Database Documentation](https://docs.oracle.com/en/database/oracle/oracle-database/23/)
 
 ## Acknowledgements
 * **Authors** - Uma Kumar
-* **Contributors** - Linda Foinding
 * **Last Updated By/Date** - Taylor Zheng, Uma Kumar, Deion Locklear, Daniet Hart, July 2026