Complete List of 30-Day RAG Best Practices Cheat Sheets Series
## Week 1: Foundations and Core Concepts
Day 1: The Evolution of RAG - From Naive Retrieval to Neural Information Synthesis
Explore how RAG has transformed from simple keyword matching to sophisticated neural architectures that understand context and semantics.
Day 2: Dense vs. Sparse Retrieval - When Traditional Search Meets Modern Embeddings
Compare BM25, TF-IDF against dense vector retrieval using models like BGE, E5, and Sentence-BERT in real-world scenarios.
Day 3: Embedding Models Showdown - Choosing the Right Foundation for Your RAG System
Analyze performance differences between OpenAI's text-embedding-3, Cohere's Embed v3, and open-source alternatives like BGE-M3.
Day 4: Chunk Size Optimization - The Goldilocks Problem in Document Segmentation
Investigate how chunk size affects retrieval accuracy and generation quality, with practical guidelines for different document types.
Day 5: Vector Databases at Scale - Pinecone vs. Weaviate vs. Chroma in Production
Compare performance, cost, and scalability of leading vector databases with real benchmarks and use case scenarios.
Day 6: Retrieval Metrics That Matter - Beyond Cosine Similarity
Examine advanced metrics like MRR, NDCG, and recall@k, plus new evaluation frameworks for RAG system assessment.
Day 7: The Anatomy of RAG Failure - Common Pitfalls and Diagnostic Techniques
Identify typical failure modes in RAG systems and develop systematic approaches to debugging retrieval and generation issues.
## Week 2: Advanced Retrieval Techniques
Day 8: Hybrid Search Strategies - Combining Dense and Sparse Retrieval for Maximum Recall
Implement fusion techniques like RRF (Reciprocal Rank Fusion) and weighted combinations for improved retrieval performance.
Day 9: Query Expansion and Rewriting - Making User Intent Crystal Clear
Explore techniques like HyDE (Hypothetical Document Embeddings) and query2doc for enhanced retrieval accuracy.
Day 10: Multi-Vector Retrieval - Beyond Single Embedding Per Document
Implement ColBERT-style late interaction models and multi-vector approaches for fine-grained document matching.
Day 11: Semantic Caching in RAG - Speed Without Sacrificing Relevance
Design intelligent caching strategies that understand semantic similarity to reduce latency and costs.
Day 12: Cross-Modal RAG - Retrieving Images, Tables, and Code Alongside Text
Build systems that can retrieve and reason over multiple content types using unified embedding spaces.
Day 13: Temporal RAG - Handling Time-Sensitive Information Retrieval
Address challenges in retrieving time-sensitive information and maintaining temporal consistency in responses.
Day 14: Personalized RAG - Adapting Retrieval to Individual User Contexts
Implement user-specific retrieval strategies while maintaining privacy and avoiding filter bubbles.
## Week 3: Generation and Reasoning
Day 15: Prompt Engineering for RAG - Crafting Context-Aware Instructions
Design prompts that effectively utilize retrieved context while maintaining reasoning capabilities.
Day 16: Citation and Attribution - Building Trust Through Transparent Sourcing
Implement reliable citation mechanisms and evaluate attribution accuracy in generated responses.
Day 17: Multi-Step Reasoning with RAG - Beyond Single-Hop Question Answering
Explore iterative retrieval strategies for complex reasoning tasks requiring multiple information sources.
Day 18: RAG with Long Context Models - Leveraging 1M+ Token Windows Effectively
Adapt RAG strategies for models with extended context windows and determine optimal retrieval strategies.
Day 19: Factual Consistency in RAG - Preventing Hallucinations in Generated Content
Implement verification mechanisms to ensure generated content remains faithful to retrieved sources.
Day 20: RAG for Code Generation - Retrieving Documentation and Examples
Build specialized RAG systems for software development that retrieve relevant code examples and documentation.
Day 21: Multilingual RAG - Cross-Language Retrieval and Generation
Address challenges in building RAG systems that work across multiple languages and cultural contexts.
## Week 4: Optimization and Production
Day 22: RAG System Latency Optimization - Sub-Second Response Times
Implement caching, parallelization, and model optimization techniques for production-ready RAG systems.
Day 23: Cost Optimization in RAG - Balancing Performance and Economics
Analyze cost structures and implement strategies to reduce embedding, storage, and inference costs.
Day 24: RAG Evaluation Frameworks - Measuring What Matters
Design comprehensive evaluation pipelines using RAGAS, LlamaIndex evaluators, and custom metrics.
Day 25: A/B Testing RAG Systems - Data-Driven Optimization
Implement statistical frameworks for testing retrieval strategies and measuring user satisfaction.
Day 26: RAG Security and Privacy - Protecting Sensitive Information
Address security vulnerabilities and implement privacy-preserving techniques in enterprise RAG deployments.
Day 27: RAG Observability - Monitoring and Debugging Production Systems
Build comprehensive monitoring systems to track retrieval quality, generation performance, and user interactions.
## Week 5: Emerging Trends and Future Directions
Day 28: Agentic RAG - Autonomous Information Seeking and Reasoning
Explore how AI agents can autonomously plan retrieval strategies and iteratively refine information gathering.
Day 29: GraphRAG and Knowledge Graphs - Structured Knowledge Retrieval
Implement Microsoft's GraphRAG and other knowledge graph-enhanced retrieval systems.
Day 30: The Future of RAG - Trends Shaping the Next Generation
Synthesize emerging research directions including learned sparse retrieval, end-to-end optimization, and multimodal reasoning.