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 hours·intermediate
Tools: FastAPI/Streamlit/Next.js, LLM API (Claude/GPT), Postgres/SQLite for conversation history, Guardrails/content filtering, Docker for deployment
Market relevance — share of job ads asking for this
What employers mean
You should be able to…
- Build a full chat UI + backend that streams LLM responses in real time
- Persist conversation history per user and reload it across sessions
- Add guardrails: refuse out-of-scope requests, filter unsafe content, cite sources when relevant
- Deploy the assistant somewhere reachable (Render/Fly.io/Vercel/EC2) not just localhost
- Handle concurrent users without one user's slow request blocking another
- Add basic auth/rate limiting so the deployed assistant isn't wide open to abuse
- 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
Practice
Deployed FAQ assistant for a local Indian business
Build and deploy a chat assistant (a web UI is fine; WhatsApp via a Twilio/Gupshup sandbox is a stretch goal) for a realistic Indian small business, such as a kirana store chain or a coaching institute, that answers FAQs, remembers context within a session, and refuses to answer questions outside its scope (no medical/legal advice). Deploy it publicly and add basic rate limiting.
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 actually open and chat with
- Public GitHub repo with README covering architecture diagram, deployment steps, and guardrail examples
- Screen recording or GIF of a real conversation including a refused out-of-scope question
- Logs/analytics screenshot showing real usage or load-test results
- A note on cost per conversation and how rate limiting keeps it bounded
Interview
Questions you'll get asked
- Walk me through the architecture of a chatbot you've built end-to-end — frontend to LLM to database.
- How do you stream tokens from your backend to the browser in real time?
- How would you prevent a public-facing chat assistant from being used for unrelated/abusive requests?
- How do you persist and reload conversation history so a user can continue a chat after closing the tab?
- What guardrails would you add before letting a support chatbot go live for real customers?
- How do you handle multiple concurrent users hitting your chatbot backend without one blocking another?
- What would you monitor/log in production to know if your chatbot is actually working well?