RAG in Practice: Vector DB Selection, Vectorization, Hybrid Retrieval
Three scenarios, 6 real Skills, the path from demo to production
Deep Skill Finder
Sound familiar?
People claim they know RAG in interviews, then fall apart at follow-up questions. Chunking and embeddings into a vector store is the easy half — prompt design and generation control is where it gets hard.
source: rednote · RAG learning
Learning RAG feels impossible: resources are either scattered fragments or code that reads like ancient script. The gap between what is RAG and a working project is huge.
source: Reddit · r/MachineLearning
My RAG app is live but retrieval keeps missing. The answer is in the documents, the user asks about it, and the system still cannot recall it. No idea which layer to fix.
source: GitHub · RAG issues
Real voices scattered across Reddit / Hacker News / X / dev forums — stitched together, this is what people go through when doing "RAG In Practice".
Lazy path · recommended
Install deep-skill-finder once — let your Agent find Skills for you
Whatever "RAG In Practice" task you have, MEYO already has the matching Skills. You don't need to hunt one by one — install deep-skill-finder and your Agent will auto-search MEYO's 50,000+ Skill library on every task, recommending the best match. You just say what you want to do — matching Skills is the Agent's job.
Copy → paste into Claude Code / Codex / Cursor / WorkBuddy / CatPaw · Agent installs automatically:
Please install the deep-skill-finder skill: download the archive from https://www.meyo.life/skill-finder, extract it into your local skill directory and enable it.
Or browse common-scene Skills below. Learn more about deep-skill-finder → meyo.life/skill
Three scenarios, 2 real Skills each
Install them all, or just pick 1–2 by name. Don't worry about picking wrong — they're free, uninstall anytime.
Pick Your Vector Database
Milvus, Qdrant or pgvector? Index types, recall and cost compared in one place
For the architecture selection stage
vector-database-master
TOP 1
Vector DB selection and index tuning
Deep dive into Milvus index types (IVF_FLAT, HNSW, DISKANN and more) with a four-layer performance pyramid and recall comparisons
514 users
Hands-on with mainstream vector DBs
Master Pinecone, Weaviate, Qdrant, Milvus and pgvector for RAG applications and semantic search
1112+ users
💬 Join the Deep Skill Finder Community
Trade "RAG In Practice" workflows with other Deep Skill Finder users and get install help
FAQ
Q: Do I need to learn vector databases first to get started with RAG?
A: No. At the beginner stage, understanding the main pipeline — chunk documents, vectorize, retrieve, inject into the prompt — is enough. Start with a lightweight store like Chroma and only do serious selection when you hit millions of vectors.
Q: How long does it take to build my own RAG knowledge base?
A: With the builder skills on this page, a personal knowledge base usually runs end to end in an afternoon: ingestion, vectorization and retrieval Q&A. Enterprise knowledge bases involve permissions, incremental updates and evaluation, so they take considerably longer.
Q: My RAG answers are inaccurate — which layer do I tune first?
A: Debug in this order: chunking strategy, then the retrieval layer (hybrid retrieval, reranking), then the generation layer (prompting and hallucination control). Most hit-rate problems live in chunking and retrieval. The Retrieval Optimization scenario on this page offers layer-by-layer diagnosis.
Q: What is hybrid retrieval, and do I need it?
A: Hybrid retrieval runs keyword search (BM25) and semantic vector search in parallel, then fuses rankings with an algorithm like RRF. Keyword search covers proper nouns and IDs; semantic search covers paraphrases — together they significantly improve recall. Skip it while your dataset is small.
Q: How do these Skills relate to LangChain or LlamaIndex?
A: Frameworks provide the code components of RAG; skills provide methodology, selection checklists and best practices. They complement each other: use a skill to settle the architecture and chunking strategy first, then implement with your framework of choice.
Q: How do I install these Skills?
A: Click the Copy button on each skill card and paste the full install command into your agent (such as Claude Code). Once installed, your agent will invoke the right skill automatically in matching scenarios.