📚 Just finished “RAG Made Simple: The Complete Visual Guide to Retrieval-Augmented Generation” by Nir Diamant, and it happened to be one of those rare cases where the book actually matches how I learn. What worked especially well for me are the summaries – when to use each technique, when it’s overkill, and what the trade-offs are. That comparative lens – grounding new methods against what came before is exactly what I need to internalize something. If you only have time for a sneak peek, read the appendix on debugging and choosing the right approach.

Theory vs. practice – in real production systems, no single RAG method is enough. Different techniques address different failure modes, and a stable, reliable system almost always requires combining several of them. 

Three techniques worth calling out:

🔮 HyDE (Hypothetical Document Embeddings) – my most surprising find. The idea is to generate a fake answer to the query, then use it to retrieve real documents. Elegant solution to the structural mismatch between how questions are phrased and how answers are written. “Fake it ’til you make it” as a retrieval strategy.

🎯 Dartboard Retrieval – most useful for me personally. A problem I’ve bumped into repeatedly – in RAG and in recommendation systems + where you need to balance precision with coverage across varying distance thresholds.

 🖼️ Multi-modal RAG – most overlooked, and massively underrated, usually skipped when building an MVP. Especially for internal knowledge bases: being able to retrieve diagrams, tables, screenshots, and slides alongside text is genuinely transformative. In my experience, much of an organization’s information is in decks, charts, images, etc.