Rename Section 3: Memory Systems for Context Engineering with updated notebook names

Section directory renamed:
- section-3-memory-architecture → section-3-memory-systems-for-context-engineering

Notebooks renamed:
- 01_memory_fundamentals_and_integration.ipynb → 01_working_and_longterm_memory.ipynb
- 02_memory_enhanced_rag_and_agents.ipynb → 02_combining_memory_with_retrieved_context.ipynb
- 03_memory_management_long_conversations.ipynb → 03_manage_long_conversations_with_compression_strategies.ipynb

Notebook titles updated:
- 'Memory Architecture - From Stateless RAG to Stateful Conversations' → 'Working and Long-Term Memory'
- 'Memory-Enhanced RAG and Agents' → 'Combining Memory with Retrieved Context'
- 'Memory Management - Handling Long Conversations' → 'Managing Long Conversations with Compression Strategies'

Updated all references across:
- README.md (section title, directory path, learning path)
- COURSE_SUMMARY.md (section title, notebook names, glossary)
- notebooks/README.md (section title, notebook names)
- notebooks/SETUP_GUIDE.md (section reference)
- notebooks/section-1-context-engineering-foundations/02_context_assembly_strategies.ipynb (course outline)
- notebooks/section-2-retrieved-context-engineering/01_engineering_retrieved_context_with_rag.ipynb (next steps)
- notebooks/section-2-retrieved-context-engineering/README.md (next steps)
- notebooks/section-3-memory-systems-for-context-engineering/README.md (section title, notebook references)
- notebooks/section-3-memory-systems-for-context-engineering/01_working_and_longterm_memory.ipynb (internal links)
- notebooks/section-3-memory-systems-for-context-engineering/03_manage_long_conversations_with_compression_strategies.ipynb (completion message)
- notebooks/section-4-tool-selection/02_redis_university_course_advisor_agent.ipynb (cross-references)
- notebooks/section-4-tool-selection/02_redis_university_course_advisor_agent_with_compression.ipynb (cross-references)

Changes emphasize memory as implementation technique for context engineering:
- Section 1: Context Engineering Foundations (framework)
- Section 2: Retrieved Context Engineering (RAG/vector search)
- Section 3: Memory Systems for Context Engineering (conversation/user context)
- Creates clear progression through context types and implementation techniques

Author: nkanu17

python-recipes/context-engineering/COURSE_SUMMARY.md

@@ -95,14 +95,14 @@ None (pure theory and conceptual foundation)
 
 ---
 
-### **Section 3: Memory Architecture** (4-5 hours)
+### **Section 3: Memory Systems for Context Engineering** (4-5 hours)
 
 **Notebooks**: 3 | **Prerequisites**: Sections 1-2
 
 #### Notebooks
-1. **Memory Fundamentals and Integration** - Working memory, long-term memory, Agent Memory Server
-2. **Memory-Enhanced RAG and Agents** - Combining memory with RAG, building stateful agents
-3. **Working Memory Compression** - Compression strategies for long conversations
+1. **Working and Long-Term Memory** - Working memory, long-term memory, Agent Memory Server
+2. **Combining Memory with Retrieved Context** - Combining memory with RAG, building stateful agents
+3. **Managing Long Conversations with Compression Strategies** - Compression strategies for long conversations
 
 #### Learning Outcomes
 - ✅ Implement working memory (session-scoped) and long-term memory (cross-session)
@@ -391,7 +391,7 @@ The `redis-context-course` package provides production-ready components used thr
 - **Redis Vector Search**: Using Redis for vector storage and retrieval
 - **Hybrid Search**: Combining keyword and semantic search
 
-### Memory Architecture
+### Memory Systems for Context Engineering
 - **Dual Memory System**: Working memory (session) + Long-term memory (cross-session)
 - **Memory Types**: Semantic (facts), Episodic (events), Message (conversations)
 - **Memory Extraction Strategies**: Discrete, Summary, Preferences, Custom
@@ -649,7 +649,7 @@ from redis_context_course import (
 
 #### For Beginners (3-4 weeks, 6-8 hours/week)
 1. **Week 1**: Complete Section 1 (Foundations) and Section 2 (RAG)
-2. **Week 2**: Work through Section 3 (Memory Architecture)
+2. **Week 2**: Work through Section 3 (Memory Systems for Context Engineering)
 3. **Week 3**: Build agents in Section 4 (Tool Selection & LangGraph)
 4. **Week 4**: Optimize in Section 5 (Production)
 

python-recipes/context-engineering/README.md

@@ -100,7 +100,7 @@ Build a RAG system using Redis and RedisVL for semantic course search.
 
 ---
 
-### **Section 3: Memory Architecture** (4-5 hours)
+### **Section 3: Memory Systems for Context Engineering** (4-5 hours)
 **3 notebooks** | **Prerequisites**: Sections 1-2
 
 Master dual memory systems with Agent Memory Server, including extraction and compression strategies.
@@ -194,7 +194,7 @@ context-engineering/
 │   ├── REFERENCE_AGENT_USAGE_ANALYSIS.md  # Component usage analysis
 │   ├── section-1-context-engineering-foundations/  # Section 1 notebooks
 │   ├── section-2-retrieved-context-engineering/  # Section 2 notebooks
-│   ├── section-3-memory-architecture/  # Section 3 notebooks
+│   ├── section-3-memory-systems-for-context-engineering/  # Section 3 notebooks
 │   ├── section-4-tool-selection/       # Section 4 notebooks
 │   └── section-5-optimization-production/  # Section 5 notebooks
 │
@@ -455,7 +455,7 @@ docker-compose down -v
 **Timeline**: 3-4 weeks (6-8 hours/week)
 
 1. **Week 1**: Complete Section 1 (Foundations) and Section 2 (RAG)
-2. **Week 2**: Work through Section 3 (Memory Architecture)
+2. **Week 2**: Work through Section 3 (Memory Systems for Context Engineering)
 3. **Week 3**: Build agents in Section 4 (Tool Selection & LangGraph)
 4. **Week 4**: Optimize in Section 5 (Production)
 
@@ -496,7 +496,7 @@ docker-compose down -v
 - Build RAG systems with Redis and RedisVL
 - Create course recommendation engines
 
-**Section 3: Memory Architecture**
+**Section 3: Memory Systems for Context Engineering**
 - Master dual memory systems (working + long-term)
 - Implement memory extraction strategies
 - Apply working memory compression techniques

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

@@ -128,7 +128,7 @@ Basic Concepts        → RAG Agent      → Memory Agent    → Multi-Tool Agen
 - `CourseManager` - Course search and recommendations
 - `redis_config` - Redis configuration and connection
 
-### 🧠 **Section 3: Memory Architecture**
+### 🧠 **Section 3: Memory Systems for Context Engineering**
 **Goal**: Master memory management with Agent Memory Server
 **Duration**: ~4-5 hours
 **Prerequisites**: Section 2 completed, Agent Memory Server running
@@ -147,9 +147,9 @@ Basic Concepts        → RAG Agent      → Memory Agent    → Multi-Tool Agen
 - Session management and cross-session persistence
 
 **Notebooks**:
-1. `01_memory_fundamentals_and_integration.ipynb` - Memory basics and Agent Memory Server integration
-2. `02_memory_enhanced_rag_and_agents.ipynb` - Memory extraction strategies in practice
-3. `03_memory_management_long_conversations.ipynb` - Compression strategies for long conversations
+1. `01_working_and_longterm_memory.ipynb` - Memory basics and Agent Memory Server integration
+2. `02_combining_memory_with_retrieved_context.ipynb` - Memory extraction strategies in practice
+3. `03_manage_long_conversations_with_compression_strategies.ipynb` - Compression strategies for long conversations
 
 **Reference Agent Components Used**:
 - `redis_config` - Redis configuration
@@ -319,7 +319,7 @@ Students can:
 - ✅ Generate and ingest course data into Redis
 - ✅ Create course recommendation systems with semantic search
 
-### **After Section 3: Memory Architecture**
+### **After Section 3: Memory Systems for Context Engineering**
 Students can:
 - ✅ Integrate Agent Memory Server with AI agents
 - ✅ Implement dual memory systems (working + long-term)

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

@@ -156,7 +156,7 @@ Once setup is complete:
 
 ## 🔗 Section-Specific Requirements
 
-### Section 3 & 4: Memory Architecture & Agents
+### Section 3 & 4: Memory Systems & Agents
 - ✅ Redis (for vector storage)
 - ✅ Agent Memory Server (for memory management)
 - ✅ OpenAI API key

python-recipes/context-engineering/notebooks/section-1-context-engineering-foundations/02_context_assembly_strategies.ipynb

@@ -1561,7 +1561,7 @@
     "- Hybrid search approaches\n",
     "- Optimizing retrieval performance\n",
     "\n",
-    "**Section 3: Agent Memory Architecture**\n",
+    "**Section 3: Memory Systems for Context Engineering**\n",
     "- Long-term memory systems with Redis Agent Memory Server\n",
     "- Working memory vs. long-term memory patterns\n",
     "- Memory summarization and compression\n",

python-recipes/context-engineering/notebooks/section-2-retrieved-context-engineering/01_engineering_retrieved_context_with_rag.ipynb

@@ -33,7 +33,7 @@
     "- Combine retrieved context with user and system context\n",
     "\n",
     "**Foundation for Advanced Topics:**\n",
-    "- This RAG system becomes the base for Section 3 (Memory Architecture)\n",
+    "- This RAG system becomes the base for Section 3 (Memory Systems for Context Engineering)\n",
     "- You'll add LangGraph state management and tools in later sections\n",
     "- Focus here is purely on retrieval → context assembly → generation\n",
     "\n",
@@ -1905,7 +1905,7 @@
    "source": [
     "## 🚀 What's Next?\n",
     "\n",
-    "### 🧠 Section 3: Memory Architecture\n",
+    "### 🧠 Section 3: Memory Systems for Context Engineering\n",
     "\n",
     "In this section, you built a RAG system that retrieves relevant information for each query. But there's a problem: **it doesn't remember previous conversations**.\n",
     "\n",
@@ -1930,7 +1930,7 @@
     "    ↓\n",
     "Section 2: RAG (Retrieved Context) ← You are here\n",
     "    ↓\n",
-    "Section 3: Memory Architecture (Conversation Context)\n",
+    "Section 3: Memory Systems for Context Engineering (Conversation Context)\n",
     "    ↓\n",
     "Section 4: Tool Use and Agents (Complete System)\n",
     "```\n",

python-recipes/context-engineering/notebooks/section-2-retrieved-context-engineering/README.md

@@ -139,7 +139,7 @@ By the end of this section, you'll have:
 ## Next Steps
 
 After completing this section:
-1. **Continue to Section 3: Memory Architecture** to add sophisticated Redis-based memory
+1. **Continue to Section 3: Memory Systems for Context Engineering** to add sophisticated Redis-based memory
 2. **Review your RAG agent** and identify areas for improvement
 3. **Experiment with different queries** to understand system behavior
 4. **Consider real-world applications** of RAG in your domain

…emory_fundamentals_and_integration.ipynb …ing/01_working_and_longterm_memory.ipynbpython-recipes/context-engineering/notebooks/section-3-memory-architecture/01_memory_fundamentals_and_integration.ipynb renamed to python-recipes/context-engineering/notebooks/section-3-memory-systems-for-context-engineering/01_working_and_longterm_memory.ipynb python-recipes/context-engineering/notebooks/section-3-memory-architecture/01_memory_fundamentals_and_integration.ipynb renamed to python-recipes/context-engineering/notebooks/section-3-memory-systems-for-context-engineering/01_working_and_longterm_memory.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",
-    "# 🧠 Section 3: Memory Architecture - From Stateless RAG to Stateful Conversations\n",
+    "# 🧠 Working and Long-Term Memory\n",
     "\n",
     "**⏱️ Estimated Time:** 45-60 minutes\n",
     "\n",
@@ -3110,7 +3110,7 @@
     "\n",
     "You've learned the fundamentals of memory architecture! Now it's time to put it all together.\n",
     "\n",
-    "### **Next Notebook: `02_memory_enhanced_rag_and_agents.ipynb`**\n",
+    "### **Next Notebook: `02_combining_memory_with_retrieved_context.ipynb`**\n",
     "\n",
     "In the next notebook, you'll:\n",
     "\n",
@@ -3129,7 +3129,7 @@
     "- Learn how to build production-ready agents\n",
     "- Prepare for Section 4 (adding tools like enrollment, scheduling)\n",
     "\n",
-    "**📚 Continue to:** `02_memory_enhanced_rag_and_agents.ipynb`\n",
+    "**📚 Continue to:** `02_combining_memory_with_retrieved_context.ipynb`\n",
     "\n",
     "## ⏰ Memory Lifecycle & Persistence\n",
     "\n",
@@ -3733,7 +3733,7 @@
    "source": [
     "### Hands-On Demo Coming in Notebook 2\n",
     "\n",
-    "**In the next notebook** (`02_memory_enhanced_rag_and_agents.ipynb`), we'll:\n",
+    "**In the next notebook** (`02_combining_memory_with_retrieved_context.ipynb`), we'll:\n",
     "\n",
     "1. **Demonstrate** the difference between discrete and summary strategies\n",
     "2. **Show** the same conversation processed with both strategies\n",
@@ -3803,7 +3803,7 @@
     "\n",
     "### **Next Notebook: Memory-Enhanced RAG and Agents**\n",
     "\n",
-    "**📚 Continue to: `02_memory_enhanced_rag_and_agents.ipynb`**\n",
+    "**📚 Continue to: `02_combining_memory_with_retrieved_context.ipynb`**\n",
     "\n",
     "In the next notebook, you'll:\n",
     "\n",

…/02_memory_enhanced_rag_and_agents.ipynb …ning_memory_with_retrieved_context.ipynbpython-recipes/context-engineering/notebooks/section-3-memory-architecture/02_memory_enhanced_rag_and_agents.ipynb renamed to python-recipes/context-engineering/notebooks/section-3-memory-systems-for-context-engineering/02_combining_memory_with_retrieved_context.ipynb python-recipes/context-engineering/notebooks/section-3-memory-architecture/02_memory_enhanced_rag_and_agents.ipynb renamed to python-recipes/context-engineering/notebooks/section-3-memory-systems-for-context-engineering/02_combining_memory_with_retrieved_context.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",
-    "# 🔗 Section 3: Memory-Enhanced RAG and Agents\n",
+    "# 🔗 Combining Memory with Retrieved Context\n",
     "\n",
     "**⏱️ Estimated Time:** 60-75 minutes\n",
     "\n",

…mory_management_long_conversations.ipynb …ations_with_compression_strategies.ipynbpython-recipes/context-engineering/notebooks/section-3-memory-architecture/03_memory_management_long_conversations.ipynb renamed to python-recipes/context-engineering/notebooks/section-3-memory-systems-for-context-engineering/03_manage_long_conversations_with_compression_strategies.ipynb python-recipes/context-engineering/notebooks/section-3-memory-architecture/03_memory_management_long_conversations.ipynb renamed to python-recipes/context-engineering/notebooks/section-3-memory-systems-for-context-engineering/03_manage_long_conversations_with_compression_strategies.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",
-    "# 🧠 Section 3, Notebook 3: Memory Management - Handling Long Conversations\n",
+    "# 🧠 Managing Long Conversations with Compression Strategies\n",
     "\n",
     "**⏱️ Estimated Time:** 50-60 minutes\n",
     "\n",
@@ -3647,7 +3647,7 @@
     "\n",
     "**Redis University - Context Engineering Course**\n",
     "\n",
-    "**🎉 Congratulations!** You've completed Section 3: Memory Architecture!\n",
+    "**🎉 Congratulations!** You've completed Section 3: Memory Systems for Context Engineering!\n",
     "\n",
     "You now understand how to:\n",
     "- Build memory systems for AI agents\n",