Build a RAG System with AI
The full path to a retrieval system that returns grounded answers — understand the corpus, chunk and ground it, extract and classify the metadata, then evaluate that retrieval actually works.
Overview
A RAG system lives or dies on its retrieval layer, and most of the work that determines whether it returns grounded answers or confident nonsense happens before any model is queried — in how the corpus is understood, chunked, grounded, and organized. This blueprint builds that infrastructure deliberately. It's not a support agent and not a research assistant — it's the retrieval substrate those products sit on: a pipeline that turns a pile of documents into something a model can search and answer from honestly. You analyze what's in the corpus, prepare and ground it so chunks stay meaningful, extract the metadata that makes retrieval filterable, organize it by type, and — the step most RAG projects skip — evaluate that retrieval actually surfaces the right material. Each stage is a NewPrompt playbook you can run on its own; together they carry a document set from raw files to a retrieval layer you can trust. You own the data and the infra; the blueprint makes sure the retrieval is built, not assumed.
The journey
Each stage runs a NewPrompt playbook, with a supporting resource and tool. Work them in order — the output of each stage feeds the next.
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Understand the source corpus
Before indexing anything, get a handle on what's actually in the documents — the structure, the formats, the parts that matter — so the chunking and grounding decisions later are informed instead of arbitrary.
Outcome A clear picture of what's in the corpus you'll index.
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Chunk and ground the documents
Prepare the documents for retrieval — budget chunk size to the model, ground each source so the model answers from it instead of inventing, and split on clean boundaries so a retrieved piece still makes sense out of order.
Outcome Chunked, grounded documents that hold meaning in retrieval.
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Extract the retrieval metadata
Pull the structured metadata each document carries — dates, authors, categories, identifiers — so retrieval can filter and rank on real attributes instead of similarity alone.
Outcome Structured metadata extracted to make retrieval filterable.
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Classify and organize the content
Tag the documents by type, topic, or sensitivity so retrieval can route to the right subset — the difference between searching everything and searching the part that can actually answer.
Outcome Documents classified so retrieval searches the right subset.
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Evaluate retrieval quality
The step most RAG builds skip: test that retrieval returns the right material and the answers stay grounded. Build evaluation scenarios with expected results, catch the misses and hallucinations, and set a regression guard.
Outcome Retrieval tested for accuracy and grounding, with a regression guard.
Expected outcome
A RAG retrieval layer you can trust — a corpus understood, chunked and grounded so retrieved pieces stay meaningful, enriched with extracted and classified metadata, and evaluated so you know retrieval returns the right material — the infrastructure a grounded AI product can actually be built on, instead of a vector store and a hope.