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All capabilities · LLM application development

Build an end-to-end chat assistant

A working product: UI + backend + LLM + memory + guardrails, deployed for others to use.

~20 focused hoursintermediate
Explore 6 tools for this project
Market relevance

Which roles ask for this — and how often

Share of job postings in India, per role, that name this capability.

Prompt Engineer / AI Workflow Specialist73%
What employers mean

You should be able to…

  1. Build a full chat UI + backend that streams LLM responses in real time
  2. Persist conversation history per user and reload it across sessions
  3. Add guardrails: refuse out-of-scope requests, filter unsafe content, cite sources when relevant
  4. Deploy the assistant somewhere reachable (Render/Fly.io/Vercel/EC2) not just localhost
  5. Handle concurrent users without one user's slow request blocking another
  6. Add basic auth/rate limiting so the deployed assistant isn't wide open to abuse
  7. Instrument the assistant with logging/analytics to see what users actually ask

Needs first: Build and consume REST APIs, Design and version prompts systematically

Learn — free, link-checked

The few resources that matter

Read · beginner · 40 min · docs.streamlit.io

Build Conversational Apps

Fastest way to get a real streaming chat UI in front of a user with almost no frontend code. — Streamlit
Course · intermediate · 90 min · deeplearning.ai

Building Systems with the ChatGPT API

Walks through chaining prompts, moderation, and multi-turn memory into one working system, not isolated snippets. — DeepLearning.AI (Andrew Ng, Isa Fulford)
Build from · intermediate · 90 min · github.com

OpenAI Cookbook

Battle-tested example notebooks (chat apps, vision, retries) you can lift directly into a real project. — OpenAI
Build from · intermediate · 120 min · github.com

Anthropic courses

Anthropic's own hands-on notebooks for tool use, structured extraction, and grading model output quality. — Anthropic
Tools for practice

Choose a tool for the job

Start with one tool for each part of your project. You don’t need to learn them all.

Go to the practice brief

6 tools to explore

PostgreSQL

Data · Build

Practise SQL joins and aggregations, or store application records in a relational database.

Visit toolGetting started

OpenAI API

Build · Test

Connect model calls, tool use and structured responses to your own application.

Visit toolGetting started

Claude API

Build · Test

Build model-backed features with messages, tool use and responses you can evaluate.

Visit toolGetting started

Selected for practice by RoleGPS. No paid placements or affiliate links.
Access can change; check the details before starting.

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Practice

Deployed FAQ assistant for a local Indian business

Write a 30-question FAQ for a small business you actually know - a kirana chain, a coaching institute, your family's shop - or lift one from a real business's public FAQ page. Build a chat assistant over it that answers in-scope questions, keeps context within a session, streams its replies, and politely refuses anything outside its remit (no medical, legal or financial advice). Deploy it on a free host so it has a public URL, and put rate limiting in front so an anonymous visitor cannot run up your bill. A WhatsApp channel through a Gupshup or Twilio sandbox is a stretch goal, never a requirement.

Start from

A 30-question FAQ you write for a small business you know (kirana chain, coaching institute), or one copied from a real business's public FAQ page

Milestones
  1. Write the 30-question FAQ and get single-turn answers grounded in it · ~3.5h
  2. Add session history and token-by-token streaming in the UI · ~3.5h
  3. Add scope guardrails and tune the refusal wording on out-of-scope prompts · ~3.5h
  4. Deploy to a free host with rate limiting in front of the endpoint · ~3.5h
  5. Load-test it, measure cost per conversation, write the README · ~2.5h
Done when
  • Assistant is deployed and reachable via a public URL, not just localhost
  • Conversation history persists per session and streams responses token-by-token
  • At least 3 out-of-scope prompts are correctly refused with a polite redirect, demonstrated in the README
  • Rate limiting or basic auth prevents unlimited free usage by anonymous users
Prove it

Evidence a recruiter can check

  • A live public URL a recruiter can open and chat with right now
  • A recording of a real session including an out-of-scope question being refused and redirected
  • The rate limiter in action - a log or screenshot of a burst being throttled rather than served
  • Cost per conversation measured over the load test, with the arithmetic shown
  • An architecture diagram and deployment steps complete enough for a stranger to redeploy it
Signal it

Shipped a publicly deployed FAQ chat assistant with session memory, streaming replies, scope guardrails that refuse out-of-scope questions, and rate limiting that keeps measured cost per conversation bounded.

Interview

Questions you'll get asked

  1. Walk me through the architecture of a chatbot you've built end-to-end — frontend to LLM to database.
  2. How do you stream tokens from your backend to the browser in real time?
  3. How would you prevent a public-facing chat assistant from being used for unrelated/abusive requests?
  4. How do you persist and reload conversation history so a user can continue a chat after closing the tab?
  5. What guardrails would you add before letting a support chatbot go live for real customers?
  6. How do you handle multiple concurrent users hitting your chatbot backend without one blocking another?
  7. What would you monitor/log in production to know if your chatbot is actually working well?
See where you stand for Prompt Engineer / AI Workflow Specialist