RAG expert explanations
RAG hallucinations often come from wrong or missing chunks — not from the model “making things up” in isolation. Experts stress missing data, bad chunking, weak embeddings, and retrieval that ignores the best passages.
Teams evaluate RAG in two layers: retrieval (did we fetch the right chunks?) and generation (did the answer stay faithfu
Retrieval-augmented generation (RAG) grounds a language model on retrieved documents at query time. The clearest expert
A vector database stores embeddings for similarity search; RAG is the full pipeline that retrieves passages and conditio
Chunking splits documents before embedding and retrieval. Experts warn that fixed-size splits, missing metadata boundari
Clearest explanation
Chosen for clarity and how directly it answers the question — not for views or hype.
"You might be missing data. You might be chunking them in the wrong way. You might be using an embedding model that isn't optimum. Maybe your retrieval strategy needs to change."
Pinecone engineering webinar · RAG failure analysis · 19:48
Opens a little earlier so you catch the setup
Share formats
There are a few metrics, but the most important one for us is “Recall.” Basically, for a given question, there is at least one required fact. If the retrieval step of the application found at least one context for every required fact, we mark that for a set of questions.
Weaviate · 2:41
Open moment →Practitioners converge on these themes before debating tooling choices.
Open engineering debates — compare indexed explanations before you commit to an architecture.
Citation requirements, abstention, reranking, and human review — which layer owns groundedness.
Fixed-size chunks versus semantic, structural, or agent-assisted splits with overlap tradeoffs.
Themes repeated across indexed engineering talks and practitioner writeups — not a survey, vote count, or attributed quote roundup.
Go deeper: Retrieval evaluation · Best RAG explanation · Vector DB vs RAG
Understand, then share
Save expert explanations into one investigation, compare voices, and export a shareable research brief on this device.
Use this when you need to explain RAG to someone else — save moments, compare voices, and export a brief they can read in Slack or Notion.
Teams evaluate RAG in two layers: retrieval (did we fetch the right chunks?) and generation (did the answer stay faithfu
Retrieval-augmented generation (RAG) grounds a language model on retrieved documents at query time. The clearest expert
A vector database stores embeddings for similarity search; RAG is the full pipeline that retrieves passages and conditio
Chunking splits documents before embedding and retrieval. Experts warn that fixed-size splits, missing metadata boundari
Agents plan and execute multi-step workflows with tools. RAG measures whether the right text was ret
RAG is how you ground answers on documents. MCP standardizes how hosts connect models to tools and d
Chunking determines which text exists in the index at all. Reranking only reorders candidates alread
RAG reduces unsupported answers by showing the model retrieved passages. It does not eliminate hallu
RAG updates what the model can read when documents change. Fine-tuning updates how the model behaves
RAG retrieves relevant text at query time, then generates an answer grounded on that context. Practi
Weekly digest of new expert moments
Programmatic access (waitlist)
Browse hand-picked RAG and retrieval moments — same indexed corpus, organized for deep dives.
Open RAG explanation collection →Build a private notebook of timestamped moments while comparing RAG architecture choices.
Full RAG topic hub → · Compare RAG concepts → · Long-form RAG guide →