Workflow
AI Long Document Analysis Workflow
Get AI to actually read a document that's too big for one prompt — fit it to the model, split it cleanly, package the parts, and analyze them without losing the thread.
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 project 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 connects to a NewPrompt workflow 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 project makes sure the retrieval is built, not assumed.
The journey
Each stage runs a NewPrompt workflow, with a supporting resource and tool. Work them in order — the output of each stage feeds the next.
See the execution map →Define & Scope
Clarify what you're building and for whom.
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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.
Design
Design the right solution before building.
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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.
Build & Refine
Build, test, secure, and make it production-ready.
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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. -
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. -
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.
Best for
- Building retrieval that returns grounded, cited answers
- Chunking and grounding a corpus so the model stops hallucinating
- Teams adding a knowledge layer to an AI assistant
Not for
- A chatbot with no document corpus to ground in
- A simple FAQ that fits in one prompt
FAQ
What problem does RAG solve?
Hallucination and staleness. By retrieving from your corpus and grounding answers in it, the system responds from real sources instead of the model's memory.
Do I need a vector database?
Usually — retrieval needs an index. The journey focuses on the design (chunking, grounding, metadata) that makes whatever store you choose return accurate results.
How is this different from a knowledge base?
A knowledge base organizes content; a RAG system retrieves from it at query time and grounds an answer. They pair well — build the knowledge base, then the RAG layer on top.
What documents do I need before building a RAG system with AI?
You need a real source corpus to ground answers in — the actual documents the system will retrieve from. Stage 1 walks you through understanding their structure, formats, and the parts that matter, so the later chunking, metadata extraction, and classification decisions are informed by what's in the files instead of guessed.
How do I reduce hallucinations in a RAG system?
You reduce them by grounding retrieval, not by trusting the model. Stage 2 chunks documents on clean boundaries so retrieved pieces stay meaningful and each source is grounded, and Stage 5 builds evaluation scenarios that catch ungrounded answers with a regression guard. You run and judge the results yourself.
How do I validate a RAG system before production?
You validate it in Stage 5: build evaluation scenarios with expected passages, confirm retrieval surfaces the right material, and catch hallucinations before users do. The blueprint hands you the eval workflow and test-scenario prompts; you run them against your own corpus and decide when retrieval is trustworthy enough to ship.
Workflows in this project
Workflow
AI RAG Context Workflow
Prepare documents for a RAG system so retrieved answers stay accurate — budget the chunk size to the model, ground the sources against drift, and split them on clean boundaries for retrieval.
Workflow
AI Data Extraction Workflow
Turn messy text into structured data you can trust enough to feed another system — bound the source, extract the fields, force clean JSON, and validate before it flows downstream.
Workflow
AI Classification Workflow
Build a text classification step you can automate on — pull out the unit to classify, assign a label from a fixed set, and validate the label is one you actually allow.
Workflow
AI Agent Evaluation Workflow
Find out whether an AI agent behaves before users do — define what correct means, build test scenarios with expected outputs, catch failures and hallucinations, then regression-test each version.
Resources used in this project
Resource
Plan Large Document Analysis — When It Will Not Fit
A book-length document against a 200K window: the estimate exceeds the budget at both ends of the range. The plan starts from Will Not Fit, not from hope.
Resource
Context Window Planning for RAG — Budget the Retrieved Docs
RAG context is a budget with line items: retrieved documents, the question, and the answer all share one window. Plan how many chunks actually fit.
Resource
Extract Data From Text with AI
Free text in, named fields out. The extraction prompt pattern that turns any unstructured text into consistent, parseable records.
Resource
Text Classification Prompt — the Anatomy
The blocks a reliable classification prompt needs: defined labels, classification rules, edge-case rules, an ambiguity policy, and a confidence contract.
Resource
Validation Test Prompt
Required fields one at a time, invalid formats, business rules at their exact boundaries — validation tested the way users break it.
Resource
Groundedness Check Prompt
Verify a RAG answer is actually from its retrieved context — every claim traced to a retrieved passage, and any answer that outran its sources flagged.
Resource
Agent Test Scenario Prompt
Build the test set an agent has to pass — scenarios across the happy path, edges, and adversarial inputs, each paired with the expected behavior to grade against.
Resource
Hallucination Detection Prompt
Catch the confident invention — check an AI output's claims against its source and flag every statement that isn't supported, with the unsupported span quoted.
Tools used in this project
Tool
Long Input Formatter
Package source material with delimiters, citable section labels, and grounding rules — material and instructions stay separate.
Tool
Context Window Estimator
Will this fit the model's context window? Token budget planning, range-honest fit verdicts, and model comparison.
Tool
Extraction Prompt Generator
Build prompts that extract defined fields from unstructured text — emails, invoices, tickets, résumés.
Tool
Data Classification Prompt
Build classification prompts that assign labels from a closed set — with label definitions and edge-case rules.
Tool
Test Case Prompt Generator
Build test generation prompts — unit, integration, or E2E — with framework modes and edge-case coverage rules.
Guides for this project
Guide
How to count tokens in a prompt before you send it
Counting a prompt's tokens before you send it tells you whether it fits the model, what it will cost, and whether the end might get cut off. Here's how to check and trim.
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