Enterprise Knowledge Assistant

The portfolio piece that combines enterprise RAG, RBAC, compliance, and observability — what Fortune 500 internal copilots actually look like.

Company Wikipedia that knows what you're allowed to see — interns don't get the salary spreadsheet even if they ask nicely.

Scenario

Engineer asks 'KMS rotation runbook.' ACL allows platform-docs collection → retrieves runbook → cites steps → offers Jira change ticket. Intern asks executive comp → ACL denies retrieval → policy message, no chunks leaked.

Solution

In Capstone Projects, apply Enterprise Knowledge Assistant to this scenario: Engineer asks 'KMS rotation runbook. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Do this before moving to the next module — reading alone is not enough.

Open the Code Walkthrough below and run it locally. Change one parameter related to Enterprise Knowledge Assistant (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Highlighted lines show where Enterprise Knowledge Assistant happens in the code.

1# Enterprise Knowledge Assistant — minimal example2from openai import OpenAI3
4client = OpenAI()  # create API client5
6# Ask the model to explain this topic7response = client.chat.completions.create(  # core API call for Enterprise Knowledge Assistant8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain enterprise knowledge assistant clearly."},11        {"role": "user", "content": f"What is enterprise knowledge assistant?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

• Filtering sensitive docs only in prompt
• No audit log
• Skipping approval on bulk export

Quick recap — the most important points from this module.