Rename Section 2: Retrieved Context Engineering with updated notebook name

Section directory renamed:
- section-2-rag-foundations → section-2-retrieved-context-engineering

Notebook renamed:
- 01_rag_retrieved_context_in_practice.ipynb → 01_engineering_retrieved_context_with_rag.ipynb

Notebook title updated:
- 'RAG: Retrieved Context in Practice' → 'Engineering Retrieved Context with RAG'

Updated all references across:
- README.md (section title, directory path)
- COURSE_SUMMARY.md (section title, notebook name)
- notebooks/README.md (section title, directory path)
- notebooks/section-3-memory-architecture/README.md (prerequisites, comparisons)

Changes emphasize context engineering discipline and map to framework:
- Section 1: Context Engineering Foundations (framework)
- Section 2: Retrieved Context Engineering (context type)
- Creates clear progression through the four context types

Author: nkanu17

python-recipes/context-engineering/COURSE_SUMMARY.md

@@ -60,12 +60,12 @@ None (pure theory and conceptual foundation)
 
 ---
 
-### **Section 2: RAG Foundations** (3-4 hours)
+### **Section 2: Retrieved Context Engineering** (3-4 hours)
 
 **Notebooks**: 1 | **Prerequisites**: Section 1
 
 #### Notebooks
-1. **Building RAG with Redis** - Vector embeddings, semantic search, course recommendations
+1. **Engineering Retrieved Context with RAG** - Vector embeddings, semantic search, course recommendations
 
 #### Learning Outcomes
 - ✅ Implement vector embeddings with OpenAI

python-recipes/context-engineering/README.md

@@ -79,13 +79,13 @@ Learn the foundational concepts of context engineering and the four context type
 
 ---
 
-### **Section 2: RAG Foundations** (3-4 hours)
+### **Section 2: Retrieved Context Engineering** (3-4 hours)
 **1 notebook** | **Prerequisites**: Section 1
 
 Build a RAG system using Redis and RedisVL for semantic course search.
 
 **Notebooks**:
-1. **Building RAG with Redis** - Vector embeddings, semantic search, course recommendations
+1. **Engineering Retrieved Context with RAG** - Vector embeddings, semantic search, course recommendations
 
 **Learning Outcomes**:
 - Implement vector embeddings with OpenAI
@@ -193,7 +193,7 @@ context-engineering/
 │   ├── SETUP_GUIDE.md                  # Detailed setup instructions
 │   ├── REFERENCE_AGENT_USAGE_ANALYSIS.md  # Component usage analysis
 │   ├── section-1-context-engineering-foundations/  # Section 1 notebooks
-│   ├── section-2-rag-foundations/      # Section 2 notebooks
+│   ├── section-2-retrieved-context-engineering/  # Section 2 notebooks
 │   ├── section-3-memory-architecture/  # Section 3 notebooks
 │   ├── section-4-tool-selection/       # Section 4 notebooks
 │   └── section-5-optimization-production/  # Section 5 notebooks
@@ -491,7 +491,7 @@ docker-compose down -v
 - Learn context assembly strategies
 - Grasp the importance of context engineering
 
-**Section 2: RAG Foundations**
+**Section 2: Retrieved Context Engineering**
 - Implement vector embeddings and semantic search
 - Build RAG systems with Redis and RedisVL
 - Create course recommendation engines

python-recipes/context-engineering/notebooks/README.md

@@ -102,7 +102,7 @@ Basic Concepts        → RAG Agent      → Memory Agent    → Multi-Tool Agen
 
 **Reference Agent Components Used**: None (conceptual foundation)
 
-### 🤖 **Section 2: RAG Foundations**
+### 🤖 **Section 2: Retrieved Context Engineering**
 **Goal**: Build a complete RAG system with vector search and retrieval
 **Duration**: ~3-4 hours
 **Prerequisites**: Section 1 completed, Redis running, OpenAI API key
@@ -312,7 +312,7 @@ Students can:
 - ✅ Design context strategies for AI applications
 - ✅ Identify context engineering patterns in production systems
 
-### **After Section 2: RAG Foundations**
+### **After Section 2: Retrieved Context Engineering**
 Students can:
 - ✅ Build complete RAG systems with Redis and RedisVL
 - ✅ Implement vector similarity search for intelligent retrieval
@@ -528,7 +528,7 @@ enhanced-integration/
 │   ├── 02_context_assembly_strategies.ipynb
 │   └── README.md
 │
-├── section-2-rag-foundations/          # Complete RAG system
+├── section-2-retrieved-context-engineering/  # Complete RAG system
 │   ├── 01_building_your_rag_agent.ipynb
 │   └── README.md
 │

…_rag_retrieved_context_in_practice.ipynb …neering_retrieved_context_with_rag.ipynbpython-recipes/context-engineering/notebooks/section-2-rag-foundations/01_rag_retrieved_context_in_practice.ipynb renamed to python-recipes/context-engineering/notebooks/section-2-retrieved-context-engineering/01_engineering_retrieved_context_with_rag.ipynb python-recipes/context-engineering/notebooks/section-2-rag-foundations/01_rag_retrieved_context_in_practice.ipynb renamed to python-recipes/context-engineering/notebooks/section-2-retrieved-context-engineering/01_engineering_retrieved_context_with_rag.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "![Redis](https://redis.io/wp-content/uploads/2024/04/Logotype.svg?auto=webp&quality=85,75&width=120)\n",
     "\n",
-    "# RAG: Retrieved Context in Practice\n",
+    "# Engineering Retrieved Context with RAG\n",
     "\n",
     "## From Context Engineering to Retrieval-Augmented Generation\n",
     "\n",
@@ -658,7 +658,7 @@
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[1;34m🚀 Starting Course Catalog Ingestion\u001b[0m\n"
+       "\u001B[1;34m🚀 Starting Course Catalog Ingestion\u001B[0m\n"
       ]
      },
      "metadata": {},
@@ -671,7 +671,7 @@
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[32m✅ Redis connection successful\u001b[0m\n"
+       "\u001B[32m✅ Redis connection successful\u001B[0m\n"
       ]
      },
      "metadata": {},
@@ -684,7 +684,7 @@
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[33m🧹 Clearing existing data\u001b[0m\u001b[33m...\u001b[0m\n"
+       "\u001B[33m🧹 Clearing existing data\u001B[0m\u001B[33m...\u001B[0m\n"
       ]
      },
      "metadata": {},
@@ -697,7 +697,7 @@
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[32m✅ Data cleared successfully\u001b[0m\n"
+       "\u001B[32m✅ Data cleared successfully\u001B[0m\n"
       ]
      },
      "metadata": {},
@@ -710,7 +710,7 @@
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[32m✅ Loaded catalog from course_catalog_section2.json\u001b[0m\n"
+       "\u001B[32m✅ Loaded catalog from course_catalog_section2.json\u001B[0m\n"
       ]
      },
      "metadata": {},
@@ -723,7 +723,7 @@
        "</pre>\n"
       ],
       "text/plain": [
-       "   Majors: \u001b[1;36m5\u001b[0m\n"
+       "   Majors: \u001B[1;36m5\u001B[0m\n"
       ]
      },
      "metadata": {},
@@ -736,7 +736,7 @@
        "</pre>\n"
       ],
       "text/plain": [
-       "   Courses: \u001b[1;36m50\u001b[0m\n"
+       "   Courses: \u001B[1;36m50\u001B[0m\n"
       ]
      },
      "metadata": {},
@@ -773,7 +773,7 @@
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[32m✅ Ingested \u001b[0m\u001b[1;32m5\u001b[0m\u001b[32m majors\u001b[0m\n"
+       "\u001B[32m✅ Ingested \u001B[0m\u001B[1;32m5\u001B[0m\u001B[32m majors\u001B[0m\n"
       ]
      },
      "metadata": {},
@@ -866,7 +866,7 @@
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[32m✅ Ingested \u001b[0m\u001b[1;32m50\u001b[0m\u001b[32m courses\u001b[0m\n"
+       "\u001B[32m✅ Ingested \u001B[0m\u001B[1;32m50\u001B[0m\u001B[32m courses\u001B[0m\n"
       ]
      },
      "metadata": {},
@@ -879,7 +879,7 @@
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[34m📊 Verification - Courses: \u001b[0m\u001b[1;34m50\u001b[0m\u001b[34m, Majors: \u001b[0m\u001b[1;34m5\u001b[0m\n"
+       "\u001B[34m📊 Verification - Courses: \u001B[0m\u001B[1;34m50\u001B[0m\u001B[34m, Majors: \u001B[0m\u001B[1;34m5\u001B[0m\n"
       ]
      },
      "metadata": {},
@@ -892,7 +892,7 @@
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[1;32m🎉 Ingestion completed successfully!\u001b[0m\n"
+       "\u001B[1;32m🎉 Ingestion completed successfully!\u001B[0m\n"
       ]
      },
      "metadata": {},

…ooks/section-2-rag-foundations/README.md …-retrieved-context-engineering/README.mdpython-recipes/context-engineering/notebooks/section-2-rag-foundations/README.md renamed to python-recipes/context-engineering/notebooks/section-2-retrieved-context-engineering/README.md python-recipes/context-engineering/notebooks/section-2-rag-foundations/README.md renamed to python-recipes/context-engineering/notebooks/section-2-retrieved-context-engineering/README.md

@@ -2,7 +2,7 @@
 
 ## Overview
 
-This section teaches **memory-enhanced context engineering** by building on Section 2's RAG system. You'll learn how to add **working memory** (conversation history) and **long-term memory** (persistent knowledge) to create stateful, personalized conversations.
+This section teaches **memory-enhanced context engineering** by building on Section 2's retrieved context system. You'll learn how to add **working memory** (conversation history) and **long-term memory** (persistent knowledge) to create stateful, personalized conversations.
 
 ## Learning Objectives
 
@@ -11,13 +11,13 @@ By the end of this section, you will:
 1. **Understand** why memory is essential for context engineering (the grounding problem)
 2. **Implement** working memory for conversation continuity
 3. **Use** long-term memory for persistent user knowledge
-4. **Integrate** memory with Section 2's RAG system
+4. **Integrate** memory with Section 2's retrieved context system
 5. **Build** a complete memory-enhanced course advisor
 
 ## Prerequisites
 
 - ✅ Completed Section 1 (Context Engineering Foundations)
-- ✅ Completed Section 2 (RAG Foundations)
+- ✅ Completed Section 2 (Retrieved Context Engineering)
 - ✅ Redis instance running
 - ✅ Agent Memory Server running (see reference-agent/README.md)
 - ✅ OpenAI API key configured
@@ -81,7 +81,7 @@ User Query
 1. **System Context** (Static) - ✅ Section 2
 2. **User Context** (Dynamic, User-Specific) - ✅ Section 2 + Long-term Memory
 3. **Conversation Context** (Dynamic, Session-Specific) - ✨ **Working Memory**
-4. **Retrieved Context** (Dynamic, Query-Specific) - ✅ Section 2 RAG
+4. **Retrieved Context** (Dynamic, Query-Specific) - ✅ Section 2
 
 ## Technology Stack
 
@@ -93,7 +93,7 @@ User Query
 
 ## Key Differences from Section 2
 
-| Feature | Section 2 (Stateless RAG) | Section 3 (Memory-Enhanced RAG) |
+| Feature | Section 2 (Retrieved Context) | Section 3 (Memory-Enhanced) |
 |---------|---------------------------|----------------------------------|
 | Conversation History | ❌ None | ✅ Working Memory |
 | Multi-turn Conversations | ❌ Each query independent | ✅ Context carries forward |