Pithy Cyborg | AI News Made Simple
Pithy Cyborg | AI News Made Simple
Retrieval augmented generation is one of the most useful ideas in modern AI, but it is also one of the easiest to misunderstand. In this category, I explore RAG pipelines, semantic search, embeddings, stale indexes, retrieval failures, long-context tradeoffs, and the production problems that appear when AI systems need external knowledge.
A lot of RAG content online makes it sound cleaner than it really is. I focus on what breaks, why it breaks, and how engineers can think more clearly about search, retrieval, and grounded answers in real applications.
Browse the articles below to explore RAG and AI search systems.
Hybrid search combines dense vector retrieval with sparse keyword retrieval to get the best of both approaches. In theory, it should outperform either method alone. In practice, it still misses …
Continue Reading about Why Does Hybrid Search Still Miss the Best Passage for Obvious Queries? →
Chunk overlap is added to RAG pipelines to prevent answers from being split across chunk boundaries. The logic is sound. The implementation often produces the opposite effect: duplicate or …
Continue Reading about Can Chunk Overlap Secretly Hurt Retrieval Quality Instead of Helping It? →
Retrieval and generation are two separate steps with two separate failure modes. Retrieving the right document means your retrieval system is working. Producing the wrong answer from that document …
Continue Reading about Why Does My RAG App Return the Right Document but the Wrong Answer? →
