DecisionMap

A practical protocol for using AI to map complex business and product decisions.

DecisionMap is a protocol + prompt toolkit, not a SaaS product and not a new AI model.

It is a structured way to think through decisions where there is no single correct answer, only trade-offs under uncertainty.

---

What it is

DecisionMap helps you turn messy, high-stakes business, product, market, and marketing situations into a map of strategic options.

It is especially useful when the problem is not lack of intelligence, but a fragmented or distorted picture of reality.

Instead of asking:

“What should we do?”

it reframes the problem as:

“What are our real options, and what does each of them cost?”

Each option is mapped by:

• expected upside
• cost (money, time, reputation, risk)
• required resources
• likely reactions from competitors, customers, or partners
• short / mid / long-term effects
• assumptions
• breakpoints (where it fails)
• signals to monitor

The core output is not a final answer.

It is a working strategic hypothesis, with visible trade-offs.

---

What it is not

DecisionMap is not:

• a chatbot that gives advice
• a prediction engine
• a replacement for decision-makers
• a “smart agent” that thinks for you

It does not:

• guarantee outcomes
• remove uncertainty
• make decisions on your behalf

And it is intentionally out of scope for:

• military or political conflict
• legal or medical advice
• financial investment decisions
• M&A, layoffs, or HR restructuring

---

When to use it

Use DecisionMap when:

• you have multiple plausible strategies and no clear winner
• the decision involves trade-offs, not right vs wrong
• competitors or external reactions matter
• you feel stuck because the picture is unclear

---

How it differs from a normal AI chat

Many AI chats jump from context to a clean recommendation too quickly.

DecisionMap forces a disciplined process:

1. clarify the problem
2. separate facts, assumptions, interpretations, and unknowns
3. ask only questions that can change the strategy map
4. build multiple realistic strategies
5. compare trade-offs, resources, risks, and breakpoints
6. pressure-test shortlisted options
7. produce a decision record and working hypothesis

This is slower, but better aligned with real strategic decisions.

---

Usage

For a full copy-paste workflow with any LLM, see USAGE.md.

Quick start (manual)

You don’t need any app.

1. Set the system prompt from prompts/system_prompt.md
2. Run 01_intake.md with your situation
3. Answer 02_clarifying_questions.md
4. Generate options via 03_strategy_map.md
5. Deep dive with 04_deep_dive.md
6. Finalize with 05_decision_summary.md

Optional: track updates using schemas/cascade_log.schema.json.

For the full step-by-step version, use USAGE.md.

---

Examples

• examples/full_run_product_launch.md — complete end-to-end example
• examples/product_launch.md — compact product launch case
• examples/competitive_response.md — compact competitive response case

---

ABVX ecosystem

DecisionMap can be used as a standalone protocol with any LLM.

Inside the ABVX ecosystem:

• lab.abvx lists it as a decision/strategy protocol artifact
• agentsgen can maintain repo-local agent docs for contributors
• SET can track/audit the repository as part of orchestration flows
• ID can optionally provide portable user context for long-running decision work

None of these integrations are required for manual use.

Integration status

• lab.abvx: added as supporting tool in Decision & Strategy Protocols (repo + landing card).
• SET: added as registry entry for tracking/audit (repo-docs baseline only; no runtime orchestration).
• ID: added as optional reference/link integration (no hard dependency).
• agentsgen: repo-local docs generated (AGENTS.md, RUNBOOK.md, .agentsgen.json).

Optional ABVX integration

• DecisionMap stays standalone and usable with any LLM.
• ID is an optional context layer, not a dependency.
• SET should track and audit this repo, but not run runtime orchestration yet.
• agentsgen is the preferred path for repo-local agent-facing docs (AGENTS.md, RUNBOOK.md, .agentsgen.json).
• lab.abvx should position DecisionMap as a supporting tool in Decision & Strategy Protocols, not as core stack infrastructure.

---

What is included in v0.1

• protocol
• reusable prompts
• JSON schemas
• manual usage workflow
• example cases
• Codex notes for a future mini-tool

What is not included yet

• web app
• hosted service
• authentication
• persistent project memory
• LLM integration
• local model runner

---

Repository structure

decision-map/
├── README.md
├── USAGE.md
├── protocol.md
├── prompts/
│   ├── system_prompt.md
│   ├── 01_intake.md
│   ├── 02_clarifying_questions.md
│   ├── 03_strategy_map.md
│   ├── 04_deep_dive.md
│   └── 05_decision_summary.md
├── schemas/
│   ├── strategy_map.schema.json
│   └── cascade_log.schema.json
├── examples/
│   ├── full_run_product_launch.md
│   ├── product_launch.md
│   └── competitive_response.md
├── codex/
│   ├── build_prompt.md
│   └── implementation_notes.md
├── LICENSE
└── .gitignore

---

Privacy

DecisionMap does not require storing data.

However:

• most hosted LLM APIs process data externally
• your input may be processed by third-party providers

For sensitive work:

• anonymize names, companies, exact financials, customer data, and internal documents
• or run in a local model / approved internal environment

---

Future direction (optional)

Future versions may include:

• project mode
• cascade logs for long-running decisions
• structured memory of decisions, assumptions, signals, outcomes, and revisions

But the core value is already here:

• options, not answers
• working hypothesis, not final truth
• clarity under uncertainty

---

Version

Current status: v0.1 protocol draft.