You said:
I am considering an idea to use somehow ChatGPT Plus (payed version) as an AI opponent and strategy/tactics advisor in turn-based hexmap wargames like "The Operational Art of War IV" (TOAW 4). What do you think about this? Any suggestions? I was thinking if maybe ChatGPT project can be better suited for one game run. Or maybe custom GPTs, I am not sure. I can eventually do the following to supply some knowledge:
- PDF manual of the game
- Map of the scenario (screenshot image, as well as further screenshots as game progresses)
- Description of the scenario
- On the map there can be counters visible with attack and defense strength
- I can supply information about turns when reinforcements arrive
Do you have also ideas what else can I bring?
ChatGPT said:
That’s a very interesting idea—and TOAW IV is actually one of the better candidates for this kind of experiment because it’s turn-based, information-dense, and already abstracted into operational concepts that an LLM can reason about.
I’ll break this down into (1) feasibility & expectations, (2) best way to structure it in ChatGPT (Projects vs Custom GPTs), (3) what you should feed it, and (4) practical workflow suggestions, plus limitations to plan around.
1. Is this a good idea? (Short answer: yes, with the right expectations)
What ChatGPT can do well
Act as a staff officer / operations planner
Provide course-of-action (COA) analysis
Identify operational objectives, risks, and trade-offs
Evaluate force ratios, terrain, supply, timing
Help avoid tunnel vision and “human bias”
What it cannot reliably do
Optimize at the micro-tactical level (exact attack sequencing)
Track dozens of counters perfectly across many turns
Replace a true rules-engine AI (it won’t simulate combat outcomes)
Think of it as:
“A human general with perfect doctrinal knowledge but imperfect situational awareness unless you feed it clean data.”
Used this way, it can be very powerful.
2. Project vs Custom GPT: what fits best?
ChatGPT Projects → best choice for a single campaign
I strongly recommend Projects for your use case.
Why Projects fit TOAW campaigns well
Persistent memory across turns
You can upload:
Manuals
Scenario briefings
Maps
Turn logs
You can evolve a single “campaign brain” over time
Easier to iteratively refine prompts
Think of it as your AI general’s war diary.
Custom GPT → better for reusable doctrine
Custom GPTs are useful if you want:
A reusable TOAW doctrine assistant
A fixed personality (e.g. “Soviet operational doctrine 1943”)
Preloaded assumptions about hex warfare
Best approach
Use a Custom GPT for:
Doctrine
Rule interpretation
General TOAW advice
Use a Project for:
A specific scenario and playthrough
You can even combine both (consult doctrine GPT → feed conclusions into the Project).
3. What to feed it (you’re already thinking in the right direction)
Your current list is solid. I’ll refine and expand it.
A. Core inputs (essential)
You already listed most of these:

Game manual (PDF)
Focus the AI’s attention by:
Explicitly stating which optional rules are ON/OFF
Highlighting combat resolution, supply, interdiction, and ZOC rules

Scenario description
Victory conditions
Time limits
Reinforcement schedule
Political / withdrawal constraints

Map images
Initial map (high resolution)
Subsequent turn screenshots
Tip: annotate screenshots when possible (arrows, circles, notes).
B. What you should definitely add (high impact)
1. Structured force summaries (text, not just images)
Images alone are limiting.
Every turn (or every few turns), provide tables like this:
FRIENDLY FORCES (Turn 6)
- III Corps: 6 divisions, avg atk 12 / def 10, supply 80%
- Armored Group A: 2 tank divisions, atk 20, fuel constrained
- Artillery Reserve: 3 units, attached to HQ X
ENEMY FORCES (Observed)
- Estimated 8 divisions in Sector North
- Strong defensive line along river hexes 23–30
This massively improves reasoning quality.
2. Command constraints
Tell it:
Stacking limits
HQ command range
Supply source locations
Rail conversion status
Movement penalties
Operational AI lives on constraints.
3. Your intent
Very important.
Example:
“My strategic intent is to break through in the south and encircle the enemy by Turn 12, even at higher casualties.”
or
“I prefer conservative play and preserving elite units.”
This helps it tailor advice.
4. Fog of War assumptions
Be explicit:
What is known
What is estimated
What might be hidden
Example:
“Enemy reserve strength east of the river is unknown.”
C. Advanced / optional but powerful inputs
1. Loss tolerance
Give numerical or qualitative limits:
“Acceptable losses: up to 20% strength”
“Avoid losing armored divisions”
2. Rules interpretation clarifications
Whenever a rule question arises:
Ask ChatGPT to quote the manual section it is using
This keeps reasoning grounded.
3. Turn log
After each turn:
TURN 7 RESULTS
- Attacks succeeded in 3/5 hexes
- Lost 1 infantry division (retreated and shattered)
- Enemy counterattack failed in sector B
This allows cumulative learning.
4. Practical workflow (this matters a lot)
Here’s a realistic and efficient loop:
Pre-game
Upload:
Manual
Scenario briefing
Ask:
“Summarize key rules and scenario-specific constraints that will shape operational planning.”
Each turn cycle
Provide new screenshots
Provide structured text summary
Ask one of these:
“Evaluate current operational situation”
“Propose 2–3 COAs with risks”
“Identify enemy vulnerabilities”
“What would you do this turn and why?”
Then you execute the turn.
Periodic deep review (every 5–10 turns)
Ask:
“Are we drifting from our original strategy? What adjustments are needed?”
5. Using ChatGPT as an AI opponent (harder, but possible)
This is trickier but still doable.
Best method
Role-play:
“You are now the Axis commander. Given this map and intel, what is your likely plan?”
You can:
Ask it to predict enemy priorities
Use it to sanity-check your own assumptions

Don’t try to let it “run both sides” fully—confirmation bias will creep in.
6. Key limitations to be aware of
It won’t remember exact hex coordinates reliably over dozens of turns
Visual parsing of dense counters is imperfect
It may hallucinate rules if not grounded in the manual
It cannot truly simulate TOAW’s combat engine
Mitigation
Use text summaries
Frequently restate constraints
Ask it to reason step-by-step
7. Bottom line
This is:
Absolutely viable
Best framed as an AI general staff, not a micromanaging AI
Most effective using Projects
Dramatically improved by structured text data
If you want, next we can:
Design a standard “turn input template”
Create a doctrine prompt tailored to TOAW IV
Define a consistent AI personality (e.g., cautious Soviet, aggressive German)
This is a really cool idea—and you’re already thinking about it the right way.