8.5.1 Project Roadmap: Build a Cited Knowledge Assistant

This capstone proves you can connect knowledge, model calls, application flow, and engineering evidence into one reproducible LLM application.

See the Project Evidence Loop First

The project is not “connect a vector database.” It is a traceable loop: documents, chunks, retrieval, context, answer, citations, logs, evaluation, and improvement.

Run a Project Readiness Check

Use this checklist before calling the project done.

project = {
    "project_type": "knowledge-base assistant",
    "documents": 5,
    "eval_questions": 10,
    "citations": True,
    "empty_retrieval_handled": True,
    "failure_cases": 3,
}

ready = (
    project["documents"] >= 3
    and project["eval_questions"] >= 10
    and project["citations"]
    and project["empty_retrieval_handled"]
    and project["failure_cases"] >= 1
)

print("ready:", ready)
print("project_type:", project["project_type"])
print("evidence:", "docs, eval, citations, failures")

Expected output:

ready: True
project_type: knowledge-base assistant
evidence: docs, eval, citations, failures

If ready is False, do not add another feature yet. Complete the evidence loop first.

Learn in This Order

Step	Project	What It Trains
1	Enterprise or course knowledge base	Retrieval, permissions, citations, traceable answers
2	Intelligent assistant	Retrieval, session state, and tool calling as product features
3	RAG + finetuning system	Separate missing knowledge from unstable behavior
4	Courseware generation assistant	Document parsing, structured output, and template rendering
5	Full hands-on workshop	A minimum reproducible loop before adding real APIs or databases

If you need a guided baseline, start with 8.5.6 Hands-on: Full Chapter 8 RAG App Workshop.

Project Deliverable Standards

Deliverable	Minimum Requirement	Stronger Portfolio Version
README	Goal, run command, dependencies, and examples	Add architecture diagram, design trade-offs, cost, and retrospective
Knowledge base sample	Raw documents, chunks, metadata, and source fields	Add permission rules, document version, and update notes
Retrieval logs	Matched passages, scores, and ranking	Add failure-type statistics and before/after comparison
Answer citations	Final answers show supporting sources	Add citation faithfulness checks
Failure cases	At least one documented failure	Add 3 or more cases with cause, fix, and regression check
Evaluation	Fixed questions with pass/fail rules	Add baseline, metrics, and regression testing
Deployment note	How to run and required environment variables	Add Docker, monitoring, and fallback notes

Pass Check

You pass this chapter when the project can answer with citations, show retrieval logs, handle empty retrieval, keep evaluation cases, and explain at least one failure.

The strongest portfolio version is not the largest one. It is the version where another developer can reproduce the run, inspect the evidence, and understand how you would improve the next iteration.