Grounded AI: making an LLM draft a project charter from real data — without hallucinating

Description and use

An automation recipe for generating project charters with an LLM, grounded in real user data to prevent hallucinations.

Problem

Project charters often contain fabricated information when generated by LLMs, leading to unreliable documents in governed PM tools.

Outcome

The charter generator now produces accurate, non-hallucinated prose sections, respects existing data, and outputs deterministic JSON for easy integration.

Workflow

• 1. Collect required input documents (business case, benefits plan, agreements) and cap each to 8,000 chars, total 40,000 chars.
• 2. Build grounding context with deterministic facts and extracted prose excerpts.
• 3. Define a system prompt that (a) grounds all sections on provided data, (b) refuses to fabricate missing data, and (c) returns JSON with only specified keys.
• 4. Create a user prompt that lists target section keys and injects the grounding context.
• 5. Send prompts to the LLM and parse the JSON response.
• 6. Apply the generated prose only to empty sections, skipping live-data tables and preserving existing content.
• 7. Store the result in a draft state for human review and approval.
• 8. Ensure the AI path is optional (BYOK) and fails safely if not configured.

Evidence

The model will fabricate unless you make refusing the easier path.

Source