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Even when a route has prerender = true, Cloudflare Workers' Rollup bundler still attempts to bundle native modules, causing the build to fail. The fix is to move any native module work into a postbuild script.
Astro scoped CSS appends a scope hash to each selector, but elements rendered by <Content /> don't receive that hash — causing all prose styles to silently break.
After finishing a debug session, just say 'write this up as a post' — Claude Code extracts content from the conversation, applies a template, generates frontmatter, and commits it to the repo. No extra writing required.
For complex multi-hop questions, a single RAG search isn't enough. Agentic RAG lets the LLM evaluate whether retrieved results are sufficient — if not, it rewrites the query and searches again, forming a ReAct loop.
Your choice of embedding model directly determines RAG search quality. BGE-M3's multilingual training, 1024-dimensional vectors, and matching Reranker make it a practical pick for Traditional Chinese RAG.
Chunks too large and retrieval loses precision; too small and you lose context; hit a table and retrieval falls apart entirely. Chunking is the most underrated part of RAG — pick the wrong strategy and no amount of downstream optimization will save you.
Bi-Encoders are too coarse, Cross-Encoders are too slow — ColBERT's Late Interaction finds the sweet spot: token-level comparison between query and document, but with document vectors that can be precomputed.
When you split a document into chunks, each chunk loses its place in the original document. Contextual Retrieval solves the isolated-chunk problem by generating a per-chunk context from the whole document and prepending it at index time.
Filters too strict and getting zero results? CRAG automatically relaxes them and retries — far better than letting the LLM hallucinate an answer from general knowledge.
Vector search similarity scores don't equal relevance. Cross-Encoders use pairwise comparison to reorder results and push the truly relevant documents to the top.
Vector search finds similarity; graph search traverses relationships. When a question requires reasoning across multiple entities — crag → route → sender → grade distribution — GraphRAG outperforms standard RAG.
Vector search handles semantics; BM25 handles keywords. Combining them with RRF is what lets you handle both fuzzy queries and exact terms at the same time.
Have an LLM generate an 'ideal answer' first, then embed that hypothetical document for search — it outperforms searching with the raw query.
After each conversation, asynchronously extract likely user preferences and skill level, then automatically personalize search parameters on the next query — no manual setup required.
Ranking purely by relevance leaves you with five documents all describing the same route. MMR strikes a balance between relevance and diversity, and layering in popularity weighting makes results even more useful.
RAG doesn't have to be a rigid three-step process. It's a set of steps that can be dynamically enabled, skipped, or reordered. Pipeline as Code lets the system adapt its behavior without redeployment.
A single vector search on a complex query often misses relevant documents. Let the LLM rewrite the query into 3-5 sub-queries, run them in parallel, and recall improves significantly.
Climbing routes carry a ton of visual information (topos, wall photos) that text-only RAG misses entirely. Multimodal RAG makes images searchable and understandable.
Naive RAG works but has real problems. Advanced RAG patches those problems. Modular RAG rearchitects the whole system to be composable and configurable. Understanding all three generations is the key to understanding why modern RAG systems look the way they do.
For complex queries, have the LLM map out what information is needed and in how many steps — then execute that plan. More systematic than thinking on the fly.