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 Content Moderation System with AI
The full path to an AI content moderation system — define the policy and label taxonomy, extract signals from user content, classify it against policy, emit structured decisions, evaluate false positives and negatives, wire enforcement and review queues, review abuse risks, then ship.
Overview
Content moderation is policy enforcement on user-generated content, a different problem from creating content or answering questions. It isn't a content engine producing and publishing material, and it isn't a support agent answering a user's question — it reads what users post, decides whether it violates a defined policy, and produces a machine-actionable decision that an enforcement system or a human reviewer acts on. The hard part is precision under consequence: a false positive silences a legitimate user, a false negative lets harm through, and both are judged against a policy that has to be explicit before a model can apply it. This project builds that system end to end: it defines the moderation policy and label taxonomy, extracts the signals from user content, classifies that content against the policy with a closed, validated label set, emits structured moderation decisions a system can act on, evaluates the false positives, false negatives, and edge cases that decide whether the system is trustworthy, wires the enforcement actions and human review queues, reviews the security and abuse risks (evasion, prompt injection in user content, reviewer access), and prepares the release. It is deliberately not an AI Content Engine (creation and publishing) and not an AI Support Agent or Customer Support System (answering and ticketing) — it's classification-as-enforcement against policy. NewPrompt designs the system and generates the prompts, label schemas, and contracts for each stage; you run those in your own AI tool and own the policy, the data, and every enforcement decision. It does not moderate your content, store user posts, or run AI on its servers — and because moderation decisions carry real consequences, the journey keeps a human review queue in the loop instead of pretending the model is the final word.
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 moderation policy and label taxonomy
Frame the policy before the model touches anything — the categories of violating content, the label set, and the edge cases that decide borderline calls — so classification applies an explicit standard instead of an implied one.
Outcome A moderation policy and a closed label taxonomy the system enforces.
Build & Refine
Build, test, secure, and make it production-ready.
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Extract the signals from user content
Pull the parts of the content that matter to the policy — the text, the claims, the context — out of the surrounding noise, so the classifier judges the right unit rather than a whole messy payload.
Outcome The policy-relevant signals isolated from raw user content. -
Classify content against policy
Assign a label from the closed taxonomy with tie-breaking rules, so the system returns one allowed verdict per item consistently instead of inventing a category or hedging on a call an enforcement path can't act on.
Outcome One validated policy label per item, applied consistently. -
Produce structured moderation decisions
Shape the verdict into a strict, machine-actionable decision — label, confidence, reason, recommended action — so an enforcement system or a review queue consumes a predictable contract every time, not free-form text.
Outcome A strict moderation-decision contract an enforcement system can act on. -
Evaluate false positives, negatives, and edge cases
Build the test set the system must pass — content with known correct verdicts, including the borderline and adversarial cases — so false positives and false negatives are measured before the system acts on real user content.
Outcome Moderation accuracy measured against known and adversarial cases. -
Wire enforcement actions and review queues
Design the integrations that turn a decision into an outcome — auto-actions on high-confidence verdicts, a human review queue for the rest, webhook events into the product — with the retries and failure handling a consequential action demands.
Outcome Enforcement and review-queue integration contracts with human-in-the-loop. -
Review security and abuse risks
Review the system the way an adversary would — evasion attempts, prompt injection hidden in user content, reviewer-queue access — and back the findings with tests, because a moderation system is a target the moment it has teeth.
Outcome Evasion, injection, and reviewer-access risks reviewed for vulnerabilities.
Ship & Validate
Ship with confidence and validate results.
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Prepare the release
Cross from built to live — readiness checks, a deploy and rollback plan, and monitoring on decision quality and queue volume — so launch is deliberate and a spike in false positives or a flood of missed violations is caught fast.
Outcome The moderation system shipped with a rollback path and quality monitoring.
Expected outcome
An AI content moderation system designed end to end — a policy label taxonomy, signal extraction, validated classification against policy, structured moderation decisions, a false-positive/false-negative evaluation, enforcement and review-queue integrations, an abuse-and-security review, and a release plan — so user content is judged consistently against an explicit policy, with humans in the loop where the decision carries weight.
Best for
- Classifying user-generated content against an explicit policy
- Producing machine-actionable moderation decisions with review queues
- Teams that must measure false positives and false negatives before going live
Not for
- Producing and publishing content — that's the AI Content Engine project
- Answering user questions — that's the AI Support Agent project
FAQ
How is this different from the AI Content Engine project?
A content engine creates and publishes content. This judges content other people created against a policy — extract, classify, decide, enforce. The core is classification-as-enforcement and evaluating false positives and negatives, not production or publishing.
How is this different from an AI Support Agent?
A support agent answers a user's question. A moderation system evaluates a user's content against policy and produces a decision an enforcement system or reviewer acts on. Different job, different output: a verdict and an action, not an answer.
Does NewPrompt moderate my content automatically?
No. NewPrompt designs the system and generates the prompts, label schemas, and contracts. You run them in your own AI tool, supply the policy and the content yourself, and own every enforcement decision. The journey deliberately keeps human review queues in the loop — NewPrompt does not store content or run AI.
How do I build an AI content moderation system with AI?
You build it in stages, each run in your own AI tool. First define the moderation policy and a closed label taxonomy, then extract signals from user content, classify against policy, emit structured decisions, evaluate false positives and negatives, wire enforcement and human review queues, review abuse risks, and ship with quality monitoring.
What policy do I need before building AI content moderation?
You need an explicit policy before anything else. The first stage defines the moderation policy and a closed label taxonomy — the violation categories, the label set, and the borderline edge cases — so the model applies a written standard, not an implied one. Without that policy, classification has no benchmark to enforce or measure against.
When should humans review AI moderation decisions?
Humans stay in the loop wherever the decision carries weight. The blueprint wires auto-actions only on high-confidence verdicts and routes everything else to a human review queue, with appeals and escalation. You measure false positives and negatives, but reviewers own consequential enforcement calls — the model is never framed as the final word.
Workflows in this project
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 Integration & Webhook Workflow
Connect systems so they don't break each other — map the integration boundaries, design the event and webhook contracts, plan retries and failure handling, then document the integration.
Workflow
AI Security Review Workflow
Review code for what an attacker would do, not just what tests catch — anchor the model as a security engineer, run a threat-focused review, then back the findings with auth and input tests.
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
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
Security Code Review Prompt
Twelve security checks — injection, auth, secrets, SSRF, privilege escalation — reviewed the way an attacker would read the code.
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
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
Code Review Prompt Generator
Generate code review prompts for any scope — snippet, file, diff, or full pull request — with focused review rules.
Tool
Markdown Output Builder
Build prompts that produce documents in a fixed structure — headings, sections, and tables.
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