AI Engineering
Learn LLMs, RAG, agents, secure tools, and production AI through practical insurance examples.
Project 1 — Basic LLM chatbot
Build in this order. Each project extends a capability, not necessarily a separate repository. These are future learning projects; the notes remain in this one file. Move forward when you can explain the trade-offs and demonstrate the acceptance criteria,…
Project 2 — Streaming AI chatbot
Requirements: Show progress/text incrementally, allow cancellation, and distinguish completion from stream failure. Preserve final message state.
Project 3 — PDF RAG assistant
Requirements: Upload approved PDFs, track ingestion, ask questions, return source references, and abstain when evidence is missing.
Project 4 — Production RAG application
Requirements: Multi-tenant retrieval, incremental updates/deletions, policy versions, hybrid search, evaluated reranking, and source-aware caching.
Project 5 — Tool-calling agent
Requirements: Read claim status, lookup policy, retrieve instructions, ask for missing IDs, and escalate. Start read-only.
Project 6 — LangGraph agent
Requirements: Convert the assistant into an explicit state graph, add durable progress, clarification/approval pauses, and safe resume.
Project 7 — Multi-agent customer support
Requirements: Add specialists only for measurable policy/claim/document tasks, with clear coordinator ownership and escalation.
Project 8 — MCP server plus AI agent
Requirements: Expose reviewed customer lookup, order/policy lookup, ticket creation, and knowledge search to an approved host.
Project 9 — Deterministic plus agentic workflow
Requirements: Interpret input, prepare a draft, validate required fields, wait for review, execute once, and notify reliably.
Project 10 — Production insurance AI platform
Requirements: Combine policy RAG, claim status, guided intake, document processing, reviewed communication, staff escalation, and multi-tenant operations. Define supported tasks and explicit exclusions.
Difficulty is an editorial estimate. Completion means you chose to mark a question as understood.