Would you like me to expand any of these angles into a full technical article?

1. Introduction

The gap between proof-of-concept RAG demos and production systems

Why retrieval quality is the bottleneck, not the LLM

Scope: focus on architectural decisions, not model fine-tuning

Key thesis: production RAG is a systems engineering problem, not a prompt engineering problem

2. The Naive RAG Trap

Definition: flat vector search + top-k chunks + static prompt template

Failure modes: semantic drift, lost context, redundant retrieval, keyword mismatch

The "context window" illusion: why stuffing more tokens degrades performance

Case study: when similarity ≠ relevance

3. Retrieval Architecture Patterns

Hybrid search: combining dense (vector) and sparse (BM25) retrieval

Re-ranking pipelines: cross-encoders vs. late interaction models (ColBERT)

Query rewriting and expansion: HyDE, pseudo-relevance feedback, query decomposition

Multi-stage retrieval: coarse → fine → exact filtering strategies

GraphRAG and structured retrieval: when to augment vectors with knowledge graphs

4. Context Assembly and Windowing

Chunking strategies: fixed-size vs. semantic vs. agentic chunking

Metadata injection and filtering: leveraging structured attributes pre-generation

Context compression: selective context, relevancy scoring, and dynamic windowing

Handling long documents: hierarchical retrieval and parent-document retrieval

5. Evaluation and Observability

Why standard NLP metrics (BLEU, ROUGE) fail for RAG evaluation

Reference-free metrics: faithfulness, answer relevance, context precision/recall

Building a golden dataset and human-in-the-loop feedback loops

Observability: tracing retrieval → ranking → generation pipelines

Continuous evaluation in production: drift detection and index refresh strategies

6. Conclusion and Future Directions

Summary of the production RAG stack: retrieval quality as the foundation

Emerging trends: agentic RAG, self-correcting retrieval, and multimodal pipelines

Final recommendation: start with evaluation, then optimize retrieval, then refine generation