Phase 1: Advanced NLP Foundations (4-6 weeks)
1 Modern NLP Architecture Fundamentals
Understanding the mathematical and architectural foundations that underpin all modern language models.
- Attention Mechanisms and Multi-head Attention
- Positional Encoding Strategies
- Layer Normalization vs Batch Normalization in NLP
- Gradient Flow in Deep Language Models
1.1 Essential Papers
- Attention is All You Need (Vaswani et al., 2017) - The Foundational transformer paper.
- Layer Normalization (Ba et al., 2016) - Critical for understanding training stability
- Fixing Initialization (Zhang et al., 2019) - Advanced Initialization techniques
1.2 Books and Chapters
- “Natural Language Processing with Transfomers” by Tunstall et al. - Chapters 1-3
- “Deep Learning” by Goodfellow et al. Chapter 12 (Applications)
2 Embedding and Representation Learning
Deep understanding of how semantic meaning in encoded numerically, crucial for all downstream applications.
- Contexualized vs static embeddings
- Subword tokenization strategies (BPE, SentencePiece, WordPiece)
- Embedding space geometry and semantic relationships
- Cross-lingual embedding alignment
2.1 Essential Papers
- BERT: Pre-training of Deep Bidirectional Transformers (Delvin et al., 2018)
- Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (Reimers & Gurevych, 2019)
- SimCSE: Simple Contrastive Leaning of Sentence Embeddings (Gao et al., 2022)
- E5: Text Embeddings by Weakly-Supervised Contrastive Pre-Training (Wang et al., 2022)
2.2 Books and Chapters
- “Speech and Language Processing” by Jurafsky & Martin - Chapter 6 (Vector Semantics)
Phase 2: Transformer Architecture Deep Dive (3-4 Weeks)
3 Encoder-Only Models (BERT Family)
Understanding bidirectional context modeling and masked language modeling objectives.
- Masked Language Modeling (MLM) vs Next Sentence Prediction (NSP)
- BERT variants: RoBERTa, ALBERT, DeBERTa, DistilBERT
- Fine-tuning strategies and task-specific heads
- Probing studies and interpretability
3.1 Essential Papers
- RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)
- ALBERT: A Lite BERT for Self-Supervised Learning (Lan et al., 2019)
- DeBERTa: Decoding-enhanced BERT with Disentangled Attention (He et al., 2020)
- What Does BERT Look at? (Clark et al., 2019) - Interpretability
3.2 Books and Chapter
- “Natural Language Processing with Transformers” - Chapters 4-5
3.3 Decoder-Only Models (GPT Family)
Foundation for understanding generative ai and autoregressive language modeling.
- Autoregressive generation and sampling strategies
- Scaling laws and emergent abilities
- In-context learning mechanisms
- Architecture modifications for generation
3.4 Essential Papers
- Language Models are Unsupervised Multitask Learners (Radford et al., 2019) - GPT 2
- Language Models are Few-Shot Learners (Brown et al., 2020) - GPT 3
- Training Language Models to follow instructions with Human Feedback (Ouyang et al., 2022) - InstructGPT
- Scaling Laws for Neural Language Models (Kaplan et al., 2020)
Phase 3: Advanced Training Techniques (4-5 weeks)
4 Pre-Training and Self-Supervised Learning
Understanding how large language models acquire their foundational capabilities through machine learning techniques.
- Masked language modeling objectives
- Contrastive learning in NLP
- Curriculum learning and data ordering
- Multi-task pre-training
4.1 Essential Papers
- ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators (Clark et al., 2020)
- T5: Text-to-Text Transfer Transformer (Raffel et al., 2019)
- PaLM: Scaling Language Modeling with Autoregressive Blank Infilling (Du et al., 2021)
- GLM: General Langugage Model Pretraining with Autoregressive Blank Infilling (Du et al., 2021)
5 Fine-tuning and Alignment
Converting raw language models into helpful, harmless, and honest AI systems.
- Supervised fine-tuning (SFT)
- Reinforcement Learning from Human Feedback (RLHF)
- Direct Preference Optimization (DPO)
- Constitutional AI Approach
5.1 Essential Papers
- Training Language Models to follow instructions with human feedback (Ouyang et al., 2022)
- Constitutional AI: Harmlessness from AI Feedback (Bai et al., 2022)
- Direct Preference Optimization (Rafailov et al., 2023)
- Self-Instruct: Aligning Langugage Model with Self Generated Instructions (Wang et al., 2022)
5.2 Books & Chapters
- “Natural Language Processing with Transformers” - Chapter 7-9
Phase 4: Modern Architecture Innovations (3-4 weeks)
6 Mixture-of-Experts (MoE)
Understanding how to scale model capacity without proportional compute increases.
- Sparse expert routing mechanisms
- Load Balancing and expert utilization
- Switch Transformer and GLaM architectures
- Training instabilities and solutions
6.1 Essential Papers
- Switch Transformers: Scaling to Trillion Parameters Models (Fedus et al., 2021)
- GLaM: Efficient Scaling of Language Models with Mixture-of-Experts (Du et al., 2021)
- PaLM-2 Technical Report (Anil et al., 2023)
- Mixture-of-Experts Meets Instruction Tuning (Shen et al., 2023)
7 Long Context and Efficiency
Handling longer sequences efficiently for complex reasoning tasks.
- Linear attention mechanisms
- Sliding window attention
- Memory-efficient transformers
- Retrieval-augmented approaches
7.1 Essential Papers
- Longformer: The Long-Document Transformer (Beltagy et al., 2020)
- Big Bird: Transformers for Longer Sequences (Zaheer et al., 2020)
- FlashAttention: Fast and Memory-Efficient Exact Attention (Dao et al., 2022)
- Ring Attention with Blockwise Transformers (Liu et al., 2023)
Phase 5: Reasoning and Advanced Capabilities (4-5 Weeks)
8 Chain-of-Thought and Reasoning
Understanding how language models can perform complex multi-step reasoning.
- Chain-of-thought prompting mechanisms
- Tree of thoughts and graph-based reasoning
- Mathematical and logical reasoning capabilities
- Reasoning verification and self-correction
8.1 Essential Papers
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., 2022)
- Tree of Thoughts: Deliberate Problem Solving with Large Language Models (Yao et al., 2023)
- Self-Consistency Improves Chain of Thought Reasoning (Wang et al., 2022)
- Let’s Verify Step by Step (Lightman et al., 2023)
9 Advanced Reasoning Models
Understanding specialized architectures for complex reasoning tasks.
- QwQ model architecture and easoning capabilities
- Process supervision vs outcome supervision
- Multi-step reasoning verification
- Reasoning model evaluation metrics
9.1 Essential Papers
- Process Supervision for Reliable Reasoning (Uesato et al., 2022)
- STaR: Bootstapping Reasoning with Reasoning (Zelikman et al., 2022)
- Solving Quantitative Reasoning Problems with Language Models (Lewkowycz et al., 2022)
Phase 6: Multimodal and Specialized Models (3-4 weeks)
10 Vision-Language Models
Understanding how language models integrate with other modalities.
- Vision transformer integration
- Cross-modal attention mechanisms
- Multimodal pre-training objectives
- Visual reasoning capabilities
10.1 Essential Papers
- CLIP: Learning Transferable Visual Representations (Radford et al., 2021)
- BLIP: Bootstrapping Language-Image Pre-training (Li et al., 2022)
- GPT-4V(ision) System Card (OpenAI, 2023)
- LLaVA: Large Language and Vision Assistant (Liu et al., 2023)
11 Code Generation and Programming
Foundation for understanding agentic coding systems.
- Code representation and tokenization
- Program synthesis and code completion
- Code understanding and debugging
- Multi-language code generation
11.1 Essential Papers
- Codex: Evaluating Large Language Models Trained on Code (Chen et al., 2021)
- CodeT5: Identifier-aware Unified Pre-Trained Encoder-Decoder Models (Wang et al., 2021)
- InCoder: A Generative Model for Code Infilling and Synthesis (Fried et al., 2022)
- CodeLlama: Open Foundation Models for Code (Roziere et al., 2023)
11.2 Books and Chapters
- “The Pragmatic Programmer” by Hunt & Thomas - Chapters on programming code generation principles
Phase 7: Agentic AI and Workflows (5-6 weeks)
12 AI Agent Fundamentals
Understanding how language models can be extended into autonomous reasoning systems.
- Agent architectures and planning algorithms
- Tool use and API integration
- Memory systems and state management
- Multi-agent coordination
12.1 Essential Papers
- ReAct: Synergizing Reasoning and Acting in Language Models (Yao et al., 2022)
- Toolformer: Language Models Can Teach Themselves to Use Tools (Schick et al., 2023)
- AutoGPT: An Autonomous GPT-4 Experiment (Richards et al., 2023)
- Reflexion: Language Agents with Verbal Reinforcement Learning (Shinn et al., 2023)
13 Advanced Agentic Patterns
Mastering complex multi-step autonomous reasoning and execution.
- Planning and execution frameworks
- Self-reflection and error correction
- Multi-Modal agent capabilities
- Agent evaluation and benchmarking
13.1 Essential papers
- Plan and Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning (wang et al., 2023)
- HuggingGPT: Solving AI tasks with ChatGPT and its Friends (Shen et al., 2023)
- Cognitive Architectures for Language Agents (Sumers et al., 2023)
- WebGPT: Browser-assited question-answering with human feedback (Nakano et al., 2021)
14 Agentic Coding Systems
Understanding how AI can autonomously write, debug, and maintain complex codebases.
- Code planning and architecture generation
- Automated testing and debugging
- Code review and refactoring agents
- Multi-file project management
14.1 Essential Papers
- SWE-bench: Can Language Models Resolve Real-World GitHub Issues? (Jimenez et al., 2023)
- CodeActAgent: An Agent for Unified Code Generation, Editing and Execution (Wang et al., 2024)
- Aider: AI-Powered Coding in Your Terminal - Technical documentation
- Devin: The First AI Software Engineer - Technical reports
14.2 Books and Chapters
- “Clean Code” by Robert Martin - Chapters 1-5 (Essential for understanding code quality)
- “Design Patterns” by Gang of Four - Key patterns for agent architecture
Phase 8: Cutting-Edge Models and Applications (4-5 weeks)
15 State-of-the-Art Language Models
Understanding the latest developments in ai foundation models.
- GPT-4 and beyond capabilities
- Claude’s constitutional training approach
- Qwen model family and multilingual capabilities
- Gemini and multimodal integration
15.1 Essential Papers
- GPT-4 Technical Report (OpenAI, 2023)
- Claude’s Constitutional AI: Harmlessness from AI Feedback (Anthropic, 2022)
- Qwen Technical Report (Bai et al., 2023)
- Gemini: A Family of Highly Capable Multimodal Models (Google, 2023)
16 Evaluation and Benchmarking
Understanding how to measure and compare advanced AI capabilities.
- Reasoning benchmarks (GSM8K, MATH, etc.)
- Code generation evaluation
- Agent capability assessment
- Safety and alignment evaluation
16.1 Essential Papers
- Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models - BIG-bench (Srivastava et al., 2022)
- HumanEval: Evaluating Large Language Models Trained on Code (Chen et al., 2021)
- HellaSwag: Can a Machine Really Finish Your Sentence? (Zellers et al., 2019)
- TruthfulQA: Measuring How Models Mimic Human Falsehoods (Lin et al., 2021)
17 Production Systems
17.1 Books & Resources
- “Designing Machine Learning Systems” by Chip Huyen - Chapters 7-11
- “Building LLM Applications for Production” - Practical guides
- Hugging Face Transformers documentation - Advanced sections
Recommended Reading Order Priority
18 Tier 1 (Must Read First)
- “Attention Is All You Need” - Foundation
- “BERT: Pre-training of Deep Bidirectional Transformers”
- “Language Models are Few-Shot Learners” (GPT-3)
- “Chain-of-Thought Prompting Elicits Reasoning”
19 Tier 2 (Core Advanced Topics)
- “Training language models to follow instructions with human feedback”
- “Constitutional AI: Harmlessness from AI Feedback”
- “ReAct: Synergizing Reasoning and Acting in Language Models”
- “Switch Transformer: Scaling to Trillion Parameter Models”
20 Tier 3 (Cutting-Edge Applications)
- Model-specific technical reports (GPT-4, Claude, Qwen)
- Recent agentic coding papers
- Latest reasoning and evaluation papers
Phase 5A: Enhanced Reasoning & Advanced Alignment (6-7 weeks)
21 Test-Time Reasoning & Inference Scaling
Understanding how models improve reasoning dynamically at test time without requiring retraining.
- Test-time compute scaling (o1-style reasoning)
- Process supervision vs outcome supervision
- Verification and self-correction mechanisms
- Multi-step reasoning chain verification
21.1 Essential Papers
- DeepSeek-R1: Redefining the Landscape of Reasoning Models (DeepSeek, 2025)
- Learning to Reason without External Rewards (ArXiv, 2025)
- Reinforcement Learning with Verifiable Rewards (ArXiv, 2025)
- Programming by Backprop: LLMs Acquire Reusable Algorithmic Abstractions (ArXiv, 2025)
22 Advanced RLHF & Alignment Techniques
Enhanced understanding of cutting-edge alignment and safety methods.
- RLTHF (Targeted Human Feedback) - 2025 advancement
- Direct Preference Optimization (DPO) vs RLHF comparison
- Constitutional AI deep dive
- Mechanistic interpretability (SAEs, activation patching)
- AI Safety via debate and amplification
22.1 Essential Papers
- RLTHF: Targeted Human Feedback for LLM Alignment (2025)
- Direct Preference Optimization: Your Language Model is Secretly a Reward Model (Rafailov et al., 2023)
- Constitutional AI: Harmlessness from AI Feedback (Anthropic, 2022) - Deep dive
- Sparse Autoencoders Find Highly Interpretable Features (Cunningham et al., 2023)
- AI Safety via Debate (Irving et al., 2018)
22.2 Books and Chapters
- Reinforcement Learning from Human Feedback (Nathan Lambert, 2025) - Complete book
- “AI Safety: An Introduction” (2024) - Chapters 1-4
- “The Alignment Problem” by Brian Christian - Chapters 3-6
Phase 7A: Enhanced Agentic Systems (6-7 weeks)
23 Multi-Agent Orchestration & Advanced Frameworks
Understanding enterprise-grade agent orchestration and collaboration patterns.
- Multi-agent orchestration patterns (Microsoft AutoGen, LangGraph)
- Agent memory architectures (episodic, semantic, procedural)
- Tool-calling and function routing advanced patterns
- Agent workflow management and state persistence
23.1 Essential Papers
- Microsoft AutoGen: Multi-Agent Conversation Framework (Wu et al., 2023)
- LangGraph: Multi-Agent Workflows with State Management (LangChain, 2024)
- Agent Laboratory: Framework for Autonomous Research (Schmidgall et al., 2025)
- API-Calling Agents vs Browsing Agents: Hybrid Approaches (ArXiv, 2025)
23.2 Framework Documentation
- Microsoft AutoGen technical documentation
- LangGraph advanced patterns guide
- CrewAI orchestration patterns
- Multi-agent evaluation frameworks
24 Agent Evaluation & Benchmarking
Advanced methods for evaluating agent capabilities and performance.
- SWE-bench and coding agent evaluation
- AgentBench comprehensive assessment
- Multi-agent collaboration metrics
- Safety and alignment evaluation for agents
24.1 Essential Papers
- SWE-bench: Can Language Models Resolve Real-World GitHub Issues? (Jimenez et al., 2023)
- AgentBench: Evaluating LLMs as Agents (Liu et al., 2023)
- Evaluating Multi-Agent Collaboration (Chan et al., 2024)
- Safety Evaluation for AI Agents (Anthropic, 2024)
Phase 9A: Production & Enterprise Deployment (4-5 weeks)
25 Production Systems & Model Serving
Understanding how to deploy and scale ai systems in production environments using cloud computing.
- Model serving and inference optimization
- Load balancing and auto-scaling strategies
- Cost optimization and resource management
- Monitoring and observability frameworks
25.1 Essential Topics
- Model quantization and compression techniques
- Distributed inference and model parallelism
- Edge deployment and mobile optimization
- Real-time performance monitoring
25.2 Books and Resources
- “Designing Machine Learning Systems” by Chip Huyen - Chapters 7-11 (Complete)
- “Building LLM Applications for Production” - Advanced deployment patterns
- “Machine Learning Engineering” by Andriy Burkov - Chapters 8-10
26 Enterprise AI Governance & Safety
Understanding compliance, data governance, and safety frameworks for enterprise AI.
- Enterprise AI governance frameworks
- Red-teaming and adversarial testing methodologies
- Compliance and regulatory considerations
- Bias detection and mitigation strategies
26.1 Essential Papers
- Red Teaming Language Models to Reduce Harms (Ganguli et al., 2022)
- Enterprise AI Governance Frameworks (Microsoft, 2024)
- Bias and Fairness in Large Language Models (Blodgett et al., 2023)
- NIST AI Risk Management Framework (NIST, 2023)
26.2 Regulatory Resources
- EU AI Act compliance guidelines
- NIST AI Risk Management Framework documentation
- Industry-specific AI governance standards
Enhanced Assessment Checkpoints
27 Phase 1-2 Checkpoint: Foundation Mastery
- Explain attention mechanisms mathematically
- Compare BERT vs GPT architectures
- Implement basic transformer components
- NEW: Implement test-time reasoning chain
28 Phase 3-4 Checkpoint: Training Understanding
- Design a pre-training curriculum
- Explain RLHF vs DPO tradeoffs
- Analyze MoE routing strategies
- NEW: Implement RLTHF-style selective feedback
29 Phase 5-6 Checkpoint: Advanced Capabilities
- Implement chain-of-thought prompting
- Build a multimodal demo
- Create code generation system
- NEW: Build test-time reasoning system
30 Phase 7-8 Checkpoint: Agentic Mastery
- Design autonomous agent architecture
- Build end-to-end agentic workflow
- Evaluate and benchmark agent performance
- NEW: Implement multi-agent orchestration system
31 Phase 9 Checkpoint: Production Readiness
- Deploy scalable model serving infrastructure
- Implement comprehensive monitoring and observability
- Design enterprise governance framework
- Execute red-teaming and safety evaluation
Enhanced Success Metrics
- Can explain any modern LLM architecture in detail
- Can implement transformer components from scratch
- Can design and build agentic workflows
- Can evaluate and benchmark AI systems
- Can create production-ready AI applications
- NEW: Can implement test-time reasoning systems
- NEW: Can design multi-agent orchestration frameworks
- NEW: Can deploy enterprise-grade AI governance
Updated Timeline: 8-10 months for complete mastery with 15-20 hours/week commitment
Enhanced Reading Priority (Updated 2025)
32 Tier 1 (Must Read First - Foundations)
- “Attention Is All You Need” - Foundation
- “BERT: Pre-training of Deep Bidirectional Transformers”
- “Language Models are Few-Shot Learners” (GPT-3)
- “Chain-of-Thought Prompting Elicits Reasoning”
- NEW: “DeepSeek-R1: Redefining the Landscape of Reasoning Models”
33 Tier 2 (Core Advanced Topics - 2025 Focus)
- “Training language models to follow instructions with human feedback”
- “Constitutional AI: Harmlessness from AI Feedback”
- “ReAct: Synergizing Reasoning and Acting in Language Models”
- “Switch Transformer: Scaling to Trillion Parameter Models”
- NEW: “RLTHF: Targeted Human Feedback for LLM Alignment”
- NEW: “Direct Preference Optimization”
34 Tier 3 (Cutting-Edge Applications - 2025 Updates)
- Model-specific technical reports (GPT-4, Claude, Qwen, DeepSeek-R1)
- Recent agentic coding papers (SWE-bench, Agent Laboratory)
- Latest reasoning and evaluation papers
- NEW: Multi-agent orchestration frameworks (AutoGen, LangGraph)
- NEW: Production deployment and governance papers
35 Tier 4 (Specialized Advanced Topics)
- Mechanistic interpretability papers (SAEs, activation patching)
- Enterprise AI governance and safety frameworks
- Advanced benchmarking and evaluation methodologies
- Cutting-edge architectural innovations (MoE advances, long-context)
36 Hands-On Implementation (Updated with 2025 Projects)
Core Implementation Projects:
- Build a transformer from scratch (PyTorch)
- Fine-tune BERT for custom classification
- Implement chain-of-thought reasoning
- Create a simple coding agent
- Build a RAG system with embeddings
- Implement mixture of experts layer
- NEW: Build test-time reasoning system with verification
- NEW: Create multi-agent orchestration framework
- NEW: Implement DPO vs RLHF comparison system
- NEW: Build production monitoring dashboard with observability
- NEW: Create enterprise governance compliance checker
Phase 3A: Advanced Retrieval & Knowledge Systems (5-6 weeks)
19 Foundations & Evolution of Retrieval Systems
Understanding how retrieval systems evolved from simple RAG to agentic and hybrid architectures.
- Traditional RAG limitations (context loss, hallucinations, chunking issues)
- Evolution to Advanced RAG, Self-RAG, and Hybrid RAG
- Reflection tokens: ISREL, ISUP
- Agentic systems: planning, reflection, reasoning loops
19.1 Essential Papers
- Retrieval-Augmented Generation for Large Language Models: A Survey (Gao et al., 2023)
- Enhancing Retrieval-Augmented Generation: A Study of Best Practices (ArXiv, 2025)
- Self-RAG: Learning to Retrieve, Generate, and Critique (2024)
20 GraphRAG and Knowledge Graph Integration
Master knowledge graphs and multi-hop reasoning for RAG.
- GraphRAG fundamentals: GPT-4 based entity extraction, Leiden clustering
- Hierarchical levels (C0–C3) for abstraction
- Microsoft’s implementation and real-world GraphRAG in manufacturing
- Query-focused summarization
20.1 Essential Papers
- From Local to Global: A GraphRAG Approach to Query-Focused Summarization (Microsoft, 2024)
- KAG: Boosting LLMs in Professional Domains via Knowledge-Augmented Generation (2024)
- Retrieval-Augmented Generation with Graphs (GraphRAG) (ArXiv, 2025)
- Document GraphRAG: Knowledge Graph Enhanced Retrieval (Manufacturing Domain, 2025)
20.2 Tools & Frameworks
- Neo4j for knowledge graphs
- Microsoft GraphRAG SDK
- PyKnowledge for graph construction
- LightRAG implementation
21 Hybrid, Adaptive & Self-Reflective Retrieval
Design RAG systems that adjust to query complexity and combine dense/sparse retrieval.
- Hybrid search: BM25 + Dense + Full-text
- Adaptive RAG: routing by query complexity
- Self-RAG: reflection tokens, retrieval-critique loops
- Contrastive RAG: enhanced representation learning
21.1 Essential Papers
- Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models (KAIST, 2024)
- Contrastive In-Context Learning RAG (ArXiv, 2025)
- Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection (2024)
22 Specialized RAG Architectures
Dive into specialized RAG systems for domains, long-contexts, and semantic accuracy.
- LongRAG for document-scale retrieval
- Domain-specific systems: Golden-Retriever
- Contrastive and Contextual Semantic RAG
- Self-RAG for quality control
22.1 Essential Papers
- Long RAG: Enhanced Processing of Lengthy Documents (2025)
- Golden-Retriever: Advanced RAG for Industrial Knowledge Bases (2025)
- Contrastive Learning in RAG Systems (2025)
23 Multi-Modal & Cross-Modal Retrieval Systems
Integrate vision, audio, text, and video in RAG pipelines.
- Multi-modal RAG (Gemini 2.0, Meta LLaMA 4, Qwen 2.5 Omni)
- Vision-language (CLIP, BLIP-2, Alpha-CLIP)
- Audio-text modeling (WhisBERT, EEG-audio fusion)
- Cross-modal reasoning and long-context understanding
23.1 Essential Papers
- Multi-Modal RAG: Beyond Text Retrieval (2024)
- Thinker-Talker Architectures in Qwen (2025)
- RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval (ICLR, 2024)
24 Temporal and Causal Reasoning in Retrieval
Learn to build time-aware systems for historical and predictive tasks.
- TimeR⁴: Retrieve-Rewrite-Retrieve-Rerank
- Graphiti: bi-temporal graphs + De Bruijn GNN
- Temporal embeddings: RotateQVS
- Chain of History: LLM-guided temporal completion
24.1 Essential Papers
- TimeR⁴: Temporal Retrieval Framework (2025)
- Graphiti: Real-Time Temporal Knowledge Graphs (2024)
- Chain of History: LLM-guided Temporal Completion (2025)
25 Hierarchical Knowledge Processing Architectures
Implement multi-tier, pyramid-based, and structured models for retrieval.
- PolyRAG: 3-layer hierarchy (ontology, KG, raw chunks)
- Hierarchical Lexical Graphs (HLG)
- StatementGraphRAG and TopicGraphRAG
- HSNN: Structured modular indexing and computation sharing
25.1 Essential Papers
- Knowledge Pyramid Construction (PolyRAG) (2025)
- Hierarchical Structured Neural Networks (HSNN) (2025)
- StatementGraphRAG: Advanced Graph-Based Retrieval (2024)
26 Continual Learning & Self-Improving Systems
Build systems that learn from feedback and adapt over time.
- Reinforcement learning-based retrieval: LeReT
- Multi-Teaching-Assistant KD: MTA4DPR
- Continual learning: CLEVER with adaptive product quantization
- Self-improving retrieval mechanisms
26.1 Essential Papers
- LeReT: Learning to Retrieve by Trying (2025)
- CLEVER: Continual Learning in Evolving Retrieval (2025)
- MTA4DPR: Multi-Teaching-Assistant Knowledge Distillation (2024)
27 Federated, Cross-Domain & Cross-Lingual Systems
Build scalable, privacy-aware, and multilingual retrieval systems.
- BGE M3-Embedding: 100+ language support
- CDR-VAE: Cross-domain variational autoencoders
- FRAG: Federated RAG with homomorphic encryption
- Multiplicative caching strategies
27.1 Essential Papers
- FRAG: Federated Retrieval-Augmented Generation (2024)
- CDR-VAE: Cross-Domain Retrieval (2025)
- BGE M3-Embedding: Multilingual Retrieval (2024)
28 Real-Time & Event-Driven Retrieval Systems
Engineer real-time streaming architectures for low-latency inference.
- Apache Flink 2.0, Kappa Architecture
- Hot-warm-cold tiered storage
- Event-driven pipelines with LLMs
- Real-time IoT, fraud detection, trading systems
28.1 Essential Papers
- Real-Time Event Retrieval with Apache Flink (2025)
- Event-Driven RAG Architectures (2024)
- Low-Latency Retrieval Systems (2025)
28.2 Tools & Frameworks
- Apache Flink for real-time processing
- Apache Kafka for event streaming
- LangGraph for workflow orchestration
- Redis for caching layers
29 Evaluation, Optimization, and Production Deployment
Move from POCs to real-world scalable RAG systems.
- Evaluation metrics: comprehensiveness, diversity, faithfulness
- Cost/latency optimization strategies
- Cross-encoder reranking, contextual compression
- Production deployment patterns
29.1 Essential Papers
- RAGAS: Automated Evaluation of Retrieval Augmented Generation (2024)
- Production-Grade RAG: Scaling Retrieval Systems (2025)
- Cost-Effective RAG Optimization Strategies (2025)
29.2 Tools & Frameworks
- RAGAS for evaluation framework
- LangSmith for RAG monitoring
- TruLens for RAG evaluation
- Weights & Biases for experiment tracking
- LangChain, Haystack, LlamaIndex for deployment
Phase 3A Assessment Checkpoints
Module 19-21 Checkpoint: Advanced RAG Foundations
- Implement Self-RAG with reflection mechanisms
- Build GraphRAG system with knowledge graphs
- Create hybrid retrieval combining dense + sparse methods
Module 22-24 Checkpoint: Specialized Systems
- Deploy LongRAG for document processing
- Implement multi-modal RAG with vision + text
- Build temporal reasoning system with time-aware retrieval
Module 25-27 Checkpoint: Advanced Architectures
- Create hierarchical knowledge processing system
- Implement continual learning RAG with feedback loops
- Deploy federated RAG with privacy preservation
Module 28-29 Checkpoint: Production Systems
- Build real-time event-driven retrieval pipeline
- Implement comprehensive evaluation framework
- Deploy production-ready RAG with monitoring
Phase 3A Capstone Projects
Create 5 real-world implementations demonstrating complete mastery:
Project 1: Agentic GraphRAG System
- Query decomposition and multi-hop reasoning
- Feedback loops and self-correction mechanisms
- Integration with knowledge graphs
Project 2: Multi-Modal Adaptive RAG
- Support for video, image, audio + text
- Cross-modal retrieval and reasoning
- Dynamic adaptation to query complexity
Project 3: Real-Time Event Retrieval Pipeline
- Apache Flink/Kafka integration
- Self-correcting RAG mechanisms
- Low-latency streaming architecture
Project 4: Domain-Specific LongRAG
- Finance or healthcare document processing
- Hierarchical understanding and summarization
- Domain expertise integration
Project 5: Federated Privacy-RAG
- Encrypted search across private datasets
- Homomorphic encryption implementation
- Cross-organization knowledge sharing
Phase 3A Timeline: 5-6 weeks intensive study with 15-20 hours/week commitment