How I Structured A Multi-phase Prompt Workflow To Prevent Agent Drift, Generic Ideation, And Hallucinated Market Data - Feedback Welcomes

I have created for my community a 5-phase prompt workflow I've been refining for the past few weeks. Use case is domain-specific idea generation and business profiling, but the prompt engineering patterns translate to any structured multi-phase agent workflow.

Key design choices worth discussing:

1. Phase 0 as a role-locking context prompt. It also includes: explicit phase list, operating principles (be direct, challenge weak input, do not skip ahead), and an anti-hallucination rule ("when you don't know something, say so rather than inventing it"). The agent must acknowledge understanding before Phase 1 begins.

2. Each phase is a self-contained block. Each phase restates its purpose, its inputs (referencing prior outputs by name), its output format, and its handoff instructions. This makes the workflow robust across context-window limits and session resumption.

3. Explicit anti-patterns in the idea-generation phase. Listed as "Do NOT suggest:" — generic AI wrappers, ideas requiring enterprise sales, ideas outside a 4-week shippable window. Without this list you get the same 5 ideas every agent produces.

4. Scoring rubrics with specific anchors. Not "score 1–5 on market signal" but "1 = pure hunch, 3 = colleagues complain about this, 5 = visible paid demand (competitors charging, job postings, forum threads)." Vague anchors produce optimistic scoring.

5. Anti-hallucination discipline in Phase 5. The business profile phase is where models confidently fabricate TAM numbers. Explicit instruction: "Do not invent numbers. When you lack data, say so and recommend the 1–2 sources I should check." This alone made the output 10x more useful.

Tested with Claude Opus, GPT-4o, and Gemini Pro. File is on my profile if you want to inspect the full prompts.

What I'd love feedback on: how do you handle cross-phase context preservation in workflows longer than 4 phases? My current approach is verbose phase blocks that restate prior outputs, but that burns tokens like crazy. Curious if anyone's tried more elegant approaches. Appreciate your feedback