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.
Build a Customer Support System with AI
The full path to a support operation, not just a bot — stand up the knowledge base, route the tickets, add the AI agent, integrate your stack, close the feedback loop, evaluate, and deploy.
Overview
A support agent answers a message; a support system runs your support. The difference is everything around the agent: the knowledge it answers from, the routing that decides what it handles versus what a human does, the integrations into your helpdesk and CRM, and the feedback loop that makes the whole thing better over time. This project builds the operation. It stands up the knowledge base, sets up ticket routing, adds the AI agent as one component, integrates the support stack, closes the feedback loop, evaluates that the system behaves, and ships it. It owns support operations — it is not just an AI agent (that's the AI Support Agent project, which this builds on) and not just a knowledge base — so the result is a working support function, not a chatbot bolted onto a contact form. Each stage connects to a NewPrompt workflow you can run on its own; together they turn scattered support into a system. You own the policy and the team; the project makes sure the operation is built around the agent, not just the agent.
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 →Build & Refine
Build, test, secure, and make it production-ready.
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Stand up the support knowledge base
Prepare the documentation and help content the support system answers from — chunked and grounded for retrieval — so every answer, human or AI, comes from the same accurate source instead of tribal memory.
Outcome A grounded knowledge base the support system answers from. -
Set up ticket routing
Classify incoming tickets by intent and urgency so each one goes the right way — routine to the agent, sensitive to a human, billing to billing. Routing is the operational layer that a bare agent doesn't have.
Outcome Incoming tickets classified and routed by intent. -
Add the AI support agent
Plug in the AI agent that handles the tickets routed to it — on-brand replies grounded in the knowledge base, with a clean escalation path to a human. This is one component of the system, not the whole thing.
Outcome An AI agent handling its share of tickets with escalation. -
Integrate the support stack
Wire the system into your helpdesk, CRM, and notifications via webhooks and events — with the retries and failure handling that keep a ticket from vanishing when an integration hiccups — so support lives where your team already works.
Outcome The support system integrated with your tools via webhooks. -
Close the feedback loop
Pull themes out of resolved and escalated tickets so the operation learns — where the agent helps, where routing misfires, what the knowledge base is missing — and feeds that back into the system instead of guessing.
Outcome Themes from real tickets that drive system improvement. -
Evaluate the system
Test that the support system behaves end to end — routing sends tickets the right way, the agent answers grounded, escalation triggers when it should — with scenarios and a regression guard, before customers depend on it.
Outcome The support system tested end to end, with a regression guard. -
Run a security review
A support system handles customer data and conversations across routing, the agent, and external integrations — review those paths the way an attacker would, and back the findings with tests, before it's live.
Outcome The customer-data, support-routing, agent, and integration paths reviewed for vulnerabilities before release.
Ship & Validate
Ship with confidence and validate results.
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Deploy the operation
Ship the support system with readiness checked, rollback planned, and monitoring on the signals that matter — response times, escalation rates, deflection — so launch is deliberate and you can see the operation working.
Outcome The support operation deployed with monitoring. -
Validate the product with real evidence
With the system handling real tickets, measure whether it moved the numbers that matter — deflection, resolution time, satisfaction — against the target you set, render a hit/partial/miss verdict, and decide whether to iterate, pivot, or scale the operation.
Outcome The shipped outcome measured against its success signal, with an iterate, pivot, or scale decision documented.
Expected outcome
A customer support operation that runs — a knowledge base the system answers from, routing that sends each ticket the right way, an AI agent handling what it should, your helpdesk and CRM integrated, a feedback loop that improves it, evaluated behavior, and the whole thing deployed — a support function, not a bot bolted onto a form.
Best for
- Standing up a whole support operation, not just a single bot
- Connecting a knowledge base, ticket routing, and an AI agent into one stack
- Teams scaling support without scaling headcount linearly
Not for
- Just the agent in isolation — that is the AI Support Agent project
- A team with no support volume to operate
FAQ
How is this different from the AI Support Agent project?
Scope. The AI Support Agent builds the agent; this builds the operation around it — knowledge base, routing, integrations, and a feedback loop. Use this when you are standing up support end to end.
Do I have to build everything at once?
No — the journey is staged. Stand up the knowledge base, add routing, then the agent, then integrate. Each stage is useful on its own.
Will it improve over time?
Yes — a feedback stage and an evaluation stage are included so the system gets better instead of drifting.
How do I build a customer support system with AI?
Work through the blueprint's nine stages in order — stand up the knowledge base, set up ticket routing, add the AI agent, integrate your helpdesk and CRM via webhooks, close the feedback loop, evaluate, run a security review, then deploy and validate. NewPrompt supplies the prompts and workflows; you run them and operate the real support stack.
What knowledge base do I need before building AI customer support?
Your documentation and help content, chunked and grounded for retrieval — that is the blueprint's first stage. Before the AI agent answers anything, the knowledge base is what both humans and the agent draw from, so answers come from one accurate source instead of tribal memory. You own and curate that content; the blueprint structures how it is retrieved.
When should AI customer support escalate to a human?
Whenever a ticket is sensitive, out of policy, or beyond what the agent can ground in the knowledge base. The routing stage classifies tickets by intent and urgency — routine to the agent, sensitive to a human — and the agent stage builds the escalation path. Humans own every decision the AI hands off; you define what escalates.
Workflows in this project
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 Customer Support Workflow
Run inbound support the same way every time — triage and route the ticket, pull the details that matter, draft a reply in a consistent voice, and log the resolution for the record.
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 Customer Feedback Analysis Workflow
Turn a pile of reviews, surveys, or support comments into themes and priorities — extract the real signal, classify it by theme and sentiment, then summarize what's worth acting on.
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 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.
Workflow
AI Product Validation Workflow
Find out whether the thing you shipped actually worked — define the success metric, plan the measurement, classify the real evidence, then render a verdict and an iterate / pivot / scale decision.
Resources used in this project
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
Classify Support Tickets with AI
Billing, Technical, Account, How-To, Feature Request — ticket triage with definitions that decide the borderline cases for the model.
Resource
Customer Support Agent
Configure AI to answer support questions within your actual policy boundaries — not generic best-guess answers.
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
Categorize Customer Feedback with AI
Praise, Complaint, Feature Request, Bug Report, Question — multi-label feedback categorization where one message can carry three signals.
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
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.
Resource
Agent Evaluation Scorecard Prompt
Grade agent output the same way every time — a rubric scoring correctness, grounding, safety, tone, and completeness, with a pass threshold instead of a gut call.
Resource
Conversation Quality Evaluation Prompt
Judge the whole conversation, not one reply — evaluate a multi-turn exchange for context retention, coherence, goal completion, and recovery from misunderstanding.
Resource
Product Validation Report Prompt
Consolidate the whole validation into one shareable report — the success metric, the evidence, the hit / partial / miss verdict, and the iterate / pivot / scale decision, in a form the team and stakeholders can act on.
Resource
Product Feedback Summary Prompt
Key Findings, Recommendations, Open Risks — collected feedback compressed for the roadmap discussion, with recommendations the source actually supports.
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.
Tools used in this project
Tool
Context Window Estimator
Will this fit the model's context window? Token budget planning, range-honest fit verdicts, and model comparison.
Tool
Data Classification Prompt
Build classification prompts that assign labels from a closed set — with label definitions and edge-case rules.
Tool
System Prompt Generator
Build structured system prompts from role, tone, constraints, and model target.
Tool
JSON Output Prompt Builder
Build prompts that return structured data — JSON first, with YAML, XML, and CSV modes — parseable every time.
Tool
Extraction Prompt Generator
Build prompts that extract defined fields from unstructured text — emails, invoices, tickets, résumés.
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.
Tool
Code Review Prompt Generator
Generate code review prompts for any scope — snippet, file, diff, or full pull request — with focused review rules.
Recommended next project
Project
Build a Knowledge Base with AI
The full path to knowledge that's findable by people and AI — plan the taxonomy, structure it for search, write the articles, tag the metadata, make it retrievable, then ship it maintainable.
Related projects
Project
Build an AI Support Agent with AI
The full path to a support agent you can put in front of customers — write its instructions, ground it in your docs, route and handle tickets, then evaluate and cost-control it before it goes live.
Project
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.