Workflow
AI Product Requirements Workflow
Turn a fuzzy business need into requirements a team can build from — interrogate the need into concrete requirements, shape them as user stories, and write the PRD.
Build an AI Document Processing System with AI
The full path to an AI document processing system — define the use case, design the intake pipeline, extract fields from unstructured documents, classify and route them, pin the output contract, evaluate accuracy, then ship it monitored.
Overview
Intelligent document processing is its own problem, distinct from a data pipeline and from a retrieval system. It isn't general ETL moving rows between systems, and it isn't a RAG system answering questions — it reads unstructured documents (invoices, contracts, forms, emails, résumés) and turns them into structured, operational data: the right fields pulled out, the document type recognized and routed, the output shaped to a contract a downstream system can rely on. The failure modes are specific too — a misread field, a misrouted document, a confident extraction that's quietly wrong. This project designs that system end to end: it frames the use case and the documents in scope, designs the intake and processing pipeline, builds the extraction that pulls defined fields from messy input, classifies document types so each is routed correctly, pins the structured output contract, and evaluates extraction accuracy against the failure cases before anything runs unattended — then prepares the deployment and the monitoring that catches drift in production. NewPrompt designs the system and generates the prompts, resources, and contracts for each stage; you run those prompts in your own AI tool (ChatGPT, Claude, Gemini) and own the implementation and the data. It does not process your documents for you, run AI on its servers, or deploy anything automatically — it gives the build a correct-by-design plan instead of a pile of one-off extraction prompts.
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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Define the document processing use case
Frame what the system is for before building it — which documents it ingests, which fields matter, and what 'correct' output looks like — so every later stage serves a defined job instead of guessing.
Outcome A clear use case: the documents in scope and the fields the system must produce.
Design
Design the right solution before building.
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Design the document intake pipeline
Map how documents enter and move through processing — the ingestion, the stages each document passes, and where things can go wrong — as a designed pipeline rather than one opaque script.
Outcome An intake and processing pipeline designed stage by stage. -
Pin the structured output contract
Lock the shape of the output — a strict schema with validation rules — so downstream systems consume parseable, predictable data every time instead of whatever the model happens to return.
Outcome A strict, parseable output contract the system produces every run.
Build & Refine
Build, test, secure, and make it production-ready.
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Extract the fields from documents
Build the extraction that pulls the defined fields out of unstructured documents — handling missing values and ambiguity explicitly, so the output is the right fields, not a confident guess.
Outcome Defined fields extracted reliably from unstructured input. -
Classify document types and routing
Recognize what each document is — invoice, contract, form, email — from a fixed set, with edge-case rules, so each document is routed to the right processing path instead of one generic flow.
Outcome Documents classified by type and routed correctly. -
Evaluate extraction accuracy and failures
Build the test set the system has to pass — documents with known correct outputs, including the hard and malformed cases — so accuracy and failure modes are measured before the system runs on real volume unattended.
Outcome Extraction accuracy tested against real and failure-case documents.
Ship & Validate
Ship with confidence and validate results.
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Prepare deployment and monitoring
Plan the move to production — readiness checks, and monitoring on extraction quality and document volume — so silent drift in accuracy is caught early instead of after it has corrupted a downstream system.
Outcome A deploy-and-monitor plan that watches extraction quality in production.
Expected outcome
An AI document processing system designed end to end — an intake pipeline, field extraction from unstructured documents, document classification and routing, a structured output contract, an accuracy evaluation against real failure cases, and a deploy-and-monitor plan — so the system turns messy documents into trustworthy structured data, with a way to know when it slips.
Best for
- Extracting structured fields from unstructured documents at scale
- Classifying and routing documents by type
- Teams that need extraction accuracy evaluated before going live
Not for
- Moving data between systems generally — that's the Data Pipeline project
- Answering questions over documents — that's the RAG System project
FAQ
How is this different from the Data Pipeline project?
A data pipeline moves and transforms data between systems generally. This reads unstructured documents and turns them into structured data — extraction, classification, and a validated output contract are the core, not generic ingestion and storage.
How is this different from a RAG system?
RAG retrieves and answers questions over documents. This produces structured operational data from documents — fields, types, and a contract a downstream system consumes — and evaluates extraction accuracy, not retrieval quality.
Does NewPrompt process my documents?
No. NewPrompt designs the system and generates the prompts, resources, and contracts. You run them in your own AI tool and handle the documents and infrastructure. Nothing is uploaded to or processed by NewPrompt.
How do I build an AI document processing system?
Start by framing the use case: which documents are in scope and which fields the system must produce. This blueprint then designs the intake pipeline, builds field extraction from unstructured input, classifies and routes document types, pins a structured output contract, and evaluates accuracy before deploy. You run each stage in your own AI tool.
What document types do I need before building a document processing system?
Collect representative samples of every document type in scope before you start — invoices, contracts, forms, emails, résumés — including the messy, malformed, and edge-case ones. The use-case stage defines which fields matter per type, and those real samples become the intake design input and the evaluation set that measures extraction accuracy before go-live.
How do I validate AI document extraction accuracy?
Build a test set of documents with known-correct outputs, including the hard and malformed cases, and measure extraction against it before the system runs unattended. This blueprint's evaluation stage scores accuracy and surfaces failure modes — misread fields, misrouted documents, confident-but-wrong extractions — so you catch them before real volume, not after they corrupt downstream data.
Workflows in this project
Workflow
AI Data Pipeline & ETL Workflow
Design a pipeline that moves data without corrupting it — map the sources and ingestion, design the transformation stages, set validation and quality gates, then document the pipeline and monitoring.
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 Reliable JSON Output Workflow
Make any AI task return JSON your code can rely on — define the schema, force the model to it, validate every response, and diff the drift when a model update breaks the shape.
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.
Workflow
AI Deployment & Release Workflow
Cross the gap between 'tests pass' and 'safe in production' — assess release readiness, plan the deploy and its rollback, and set up the monitoring and launch checks before you ship, not after.
Resources used in this project
Resource
Product Requirements Assistant
Turn a rough feature idea into a structured requirement: problem statement, acceptance criteria, and what's explicitly out of scope.
Resource
Data Analyst Role Prompt
A data analyst role prompt with statistical honesty built in — clarify the decision first, treat correlation as a hypothesis, and never launder uncertainty into precision.
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
Turn a JSON Schema into a Prompt
You have the schema — fields, types, requirements. The translation into a prompt the model actually follows: schema lines, realistic example, and validation rules.
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
Production Readiness Review Prompt
SHIP or DO NOT SHIP: twelve checks on failure modes, observability, rollback, and load — the review that happens before the incident.
Tools used in this project
Tool
System Prompt Generator
Build structured system prompts from role, tone, constraints, and model target.
Tool
Role Prompt Generator
Generate expert role prompts — perspective, responsibilities, and decision criteria, not just "act as".
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
JSON Output Prompt Builder
Build prompts that return structured data — JSON first, with YAML, XML, and CSV modes — parseable every time.
Tool
Test Case Prompt Generator
Build test generation prompts — unit, integration, or E2E — with framework modes and edge-case coverage rules.
Tool
Markdown Output Builder
Build prompts that produce documents in a fixed structure — headings, sections, and tables.
Ways to Use This Project Path
Practical project ideas you can build from this base project path — each opens in the Project Advisor.
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