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Make prompts more stuctured.

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Separate tutorial.md for developers from commands for AI
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Oleksandr Bezdieniezhnykh
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docs/_metodology/01_research_phase.md
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# 1. Research phase
## 1.1 **👨‍💻Developers**: Problem statement
Discuss the problem and create in the `docs/00_problem` next files and folders:
- `problem_description.md`: Our problem to solve with the end result we want to achieve.
- `input_data`: Put to this folder all the necessary input data and expected results for the further tests. Analyze very thoroughly input data and form system's restrictions and acceptance ctiteria
- `restrictions.md`: Restrictions we have in real world in the -dashed list format.
- `acceptance_criteria.md`: Acceptance criteria for the solution in the -dashed list format.
The most important part, determines how good the system should be.
### Example:
- `problem_description.md`
We have wing type UAV (airplane). It should fly autonomously to predetermined GPS destination. During the flight it is relying on the signal form GPS module.
But when adversary jam or spoof GPS, then UAV either don't know where to fly, or fly to the wrong direction.
So, we need to achieve that UAV can fly correctly to the destination without GPS or when GPS is spoofed. We can use the camera pointing downward and other sensor data like altitude, available form the flight controller. Airplane is running Ardupliot.
- `input_data`
- orthophoto images from the UAV for the analysis
- list of expected GPS for the centers for each picture in csv format: picture name, lat, lon
- video from the UAV for the analysis
- list of expected GPS for the centers of video per timeframe in csv format: timestamp, lat, lon for each 1-2 seconds
- ...
- `restrictions.md`
- We're limiting our solution to airplane type UAVs.
- Additional weight it could take is under 1 kg.
- The whole system should cost under $2000.
- The flying range is restricted by eastern and southern part of Ukraine. And so on.
- `acceptance_criteria.md`
- UAV should fly without GPS for at least 30 km in the sunshine weather.
- UAV shoulf fly with maximum mistake no more than 40 meters from the real GPS
- UAV should fly correctly with little foggy weather with maximum mistake no more than 100 meters from the real GPS
- UAV should fly for minimum of 500 meters with missing inernal Satellite maps and the drifting error should be no more than 50 meters.
## 1.2 **✨AI Research**: Restrictions and Acceptance Criteria assesment
Put to the research context:
- `problem_description.md`
- `restrictions.md`
- `acceptance_criteria.md`
- Samples of the input data
Run it in DeepResearch tool (Gemini, DeepSeek, or other)
```
## The problem
`problem_description.md`.
## Data samples
look to the attached files (if any). They are for reference only.
## Restrictions for the input data
`restrictions.md`.
## Acceptance criteria for the output of the system:
`acceptance_criteria.md`.
## Role
You are a professional software architect
## Task: to check how realistic these acceptance criteria are. Check how critical each criterion is.
### Find out more acceptance criteria for this specific domain.
### Research the impact of each value in the acceptance criteria on the whole system quality.
### Assess acceptable ranges for each value in each acceptance criterion in the state-of-the-art solutions, and propose corrections in the next table:
- Acceptance criterion name
- Our values
- Your researched criterion values
- Status: Is the criterion added by your research to our system, modified, or removed
### Assess the restrictions we've put on the system. Are they realistic? Should we add more strict restrictions, or vise versa, add more requirements in restrictions to use our system. Propose corrections in the next table:
- Restriction name
- Our values
- Your researched restriction values
- Status: Is a restriction added by your research to our system, modified, or removed
```
**👨‍💻Developers**: Revise the result, discuss them and overwrite `acceptance_criteria.md` and `restrictions.md`
## 1.3 **✨AI Research**: Research the problem in great detail
Replace md files with actual data
Put to the research context samples of the input data
Run it in DeepResearch tool (Gemini, DeepSeek, or other)
```
The problem is described here:
`problem_description.md`
The system should process data samples in the attached files (if any). They are for reference only.
The system has next restrictions and conditions:
`restrictions.md`
- Output of our system should meet these acceptance criteria:
`acceptance_criteria.md`
You are a professional software architect.
Your task is to research all the possible ways to solve a problem, and split it to components.
Then research all the possible ways to solve components, and find out most efficient state-of-the-art solutions.
Be concise in formulating. The fewer words, the better, but do not miss any important details.
Produce the resulting solution draft in the next format:
- Short Product solution description. Brief component interaction diagram.
- Architecture solution that meets restrictions and acceptance criteria.
For each component, analyze the best possible solutions, and form a comparison table.
Each possible component solution would be a row, and has the next columns:
- Tools (library, platform) to solve component tasks
- Advantages of this solution
- Limitations of this solution
- Requirements for this solution
- How does it fit for the problem component that has to be solved, and the whole solution
- Testing strategy. Research how to cover system with tests in order to meet all the acceptance criteria. Form a list of integration functional tests and non-functional tests.
```
**👨‍💻Developer**: Revise the result from AI. Research the problem as well, and add/modify/remove some solution details in the draft.
Store it to the `docs/01_solution/solution_draft.md`
## 1.4 **✨AI Research**: Solution draft assessment
Replace md files with actual data
Run it in DeepResearch tool (Gemini, DeepSeek, or other)
```
Read carefully about the problem:
`problem_description.md`
System has next restrictions and conditions:
`restrictions.md`
Output of the system should address next acceptance criteria:
`acceptance_criteria.md`
Here is a solution draft:
`solution_draft.md`
You are a professional software architect. Your task is to identify all potential weak points and problems. Address them and find out ways to solve them. Based on your findings, form a new solution draft in the same format.
If your finding requires a complete reorganization of the flow and different components, state it.
Put all the findings regarding what was weak and poor at the beginning of the report. Put here all new findings, what was updated, replaced, or removed from the previous solution.
Then form a new solution design without referencing the previous system.
Remove Poor and Very Poor component choices from the component analysis tables, but leave Good and Excellent ones.
In the updated report, do not put "new" marks, do not compare to the previous solution draft, just make a new solution as if from scratch
Produce the new solution draft in the next format:
- Short Product solution description. Brief component interaction diagram.
- Architecture solution that meets restrictions and acceptance criteria.
For each component, analyze the best possible solutions, and form a comparison table.
Each possible component solution would be a row, and has the next columns:
- Tools (library, platform) to solve component tasks
- Advantages of this solution
- Limitations of this solution
- Requirements for this solution
- How does it fit for the problem component that has to be solved, and the whole solution
- Testing strategy. Research how to cover system with tests in order to meet all the acceptance criteria. Form a list of integration functional tests and non-functional tests.
```
**👨‍💻Developer**: Research by yourself as well - how to solve additional problems which AI figured out, and add them to the result. Rename previous `solution_draft.md` to `xx_solution_draft.md`. And then store the result draft to the `docs/01_solution/solution_draft.md`, and repeat the process. When the next solution wouldn't differ much from the previous one, store the last draft as `docs/01_solution/solution.md`
docs/_metodology/02_planning_phase.md
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# 2. Planning phase
## 2.1 **🤖📋AI plan** /gen_components
## 2.2 **🤖📋AI plan** /gen_tests
## 2.3 **🤖📋AI plan** /gen_epics
docs/_metodology/03_development_phase.md
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# 3. Development phase
## 3.1 **🤖📋AI plan**: Component Decomposition
For each component in `docs/02_components` do next:
```
Decompose `@docs/02_components/[##]_[component_name]/spec.md` to the features. If component is simple enough, make only 1 feature, if complex - separate per features. Feature can contain 0 or more APIs. Create `docs/02_components/[##]_[component_name]/[##]_[feature_name]_feature.md` with the next structure:
- Name
- Description
- Component APIs it implements if any
- External tools and service it uses for implementation if any
- Internal methods and its purposes
- Unit tests
- Integration tests
Do NOT generate any code yet, only brief explanations what should be done.
Ask as many questions as needed.
```
**👨‍💻Developer**: Answer the questions AI asked, put as many details as possible
## 3.2 **🤖AI agent**: Feature implementation
For each component in `docs/02_components/[##]_[component_name]/` folder do next:
```
Read component description `@docs/02_components/[##]_[component_name]/spec.md`.
Read all features in the folder `@docs/02_components/[##]_[component_name]`. For each feature do next:
- Implement it
- Make sure feature is connected and communicated properly with other features and existing code
- Create unit tests from the Test cases description, run it and make sure the result is a success
- Create integration test for the feature, run and make sure the result is a success
If integration tests are specified in component spec, then write them and run, and make sure that component working correctly
```
## 3.3 **🤖AI agent**: Solution composition and integration tests
```
Read all the files here `docs/03_tests/` and for each file write down tests and run it.
Compose a final test results in a csv with the next format:
- Test filename
- Execution time
- Result
Fix all problems if tests failed until we got a successful result.
In case if one or more tests was failed due to missing data from user or API or other system, ask it from developer.
Repeat test cycle until no failed tests.
```
docs/_metodology/04_refactoring_phase.md
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docs/_metodology/tutorial.md
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# 1 Research Phase
## 1.0 Problem statement
### Discuss
Discuss the problem and create in the `docs/00_problem` next files and folders:
- `problem_description.md`: Our problem to solve with the end result we want to achieve.
- `input_data`: Put to this folder all the necessary input data and expected results for the further tests. Analyze very thoroughly input data and form system's restrictions and acceptance ctiteria
- `restrictions.md`: Restrictions we have in real world in the -dashed list format.
- `acceptance_criteria.md`: Acceptance criteria for the solution in the -dashed list format.
The most important part, determines how good the system should be.
### Example:
- `problem_description.md`
We have wing type UAV (airplane). It should fly autonomously to predetermined GPS destination. During the flight it is relying on the signal form GPS module.
But when adversary jam or spoof GPS, then UAV either don't know where to fly, or fly to the wrong direction.
So, we need to achieve that UAV can fly correctly to the destination without GPS or when GPS is spoofed. We can use the camera pointing downward and other sensor data like altitude, available form the flight controller. Airplane is running Ardupliot.
- `input_data`
- orthophoto images from the UAV for the analysis
- list of expected GPS for the centers for each picture in csv format: picture name, lat, lon
- video from the UAV for the analysis
- list of expected GPS for the centers of video per timeframe in csv format: timestamp, lat, lon for each 1-2 seconds
- ...
- `restrictions.md`
- We're limiting our solution to airplane type UAVs.
- Additional weight it could take is under 1 kg.
- The whole system should cost under $2000.
- The flying range is restricted by eastern and southern part of Ukraine. And so on.
- `acceptance_criteria.md`
- UAV should fly without GPS for at least 30 km in the sunshine weather.
- UAV shoulf fly with maximum mistake no more than 40 meters from the real GPS
- UAV should fly correctly with little foggy weather with maximum mistake no more than 100 meters from the real GPS
- UAV should fly for minimum of 500 meters with missing inernal Satellite maps and the drifting error should be no more than 50 meters.
## 1.1 **✨AI Research**: Restrictions and Acceptance Criteria assesment
### Execute `/1.research/1.1_research_assesment_acceptance_criteria`
In case of external DeepResearch (Gemini, DeepSeek, or other), copypaste command's text and put to the research context:
- `problem_description.md`
- `restrictions.md`
- `acceptance_criteria.md`
- Samples of the input data
### Revise
- Revise the result, discuss it
- Overwrite `acceptance_criteria.md` and `restrictions.md`
## 1.2 **✨AI Research**: Research the problem in great detail
### Execute `/1.research/1.2_research_problem`
In case of external DeepResearch (Gemini, DeepSeek, or other), copypaste command's text and put to the research context:
- `problem_description.md`
- `restrictions.md`
- `acceptance_criteria.md`
- Samples of the input data
### Revise
- Revise the result from AI.
- Research the problem as well
- Add/modify/remove some solution details in the draft. (Also with AI)
- Store it to the `docs/01_solution/solution_draft.md`
## 1.3 **✨AI Research**: Solution draft assessment
### Execute `/1.research/1.3_solution_draft_assessment`
In case of external DeepResearch (Gemini, DeepSeek, or other), copypaste command's text and put to the research context:
- `problem_description.md`
- `restrictions.md`
- `acceptance_criteria.md`
- Samples of the input data
### Revise
- Research by yourself as well - how to solve additional problems which AI figured out, and add them to the result.
### Iterate
- Rename previous `solution_draft.md` to `{xx}_solution_draft.md`. Start {xx} from 01
- Store the new revised result draft to the `docs/01_solution/solution_draft.md`
- Repeat the process 1.3 from the beginning
When the next solution wouldn't differ much from the previous one, store the last draft as `docs/01_solution/solution.md`
# 2. Planning phase
## 2.1 **🤖📋AI plan**: Generate components
### Execute `/gen_components`
### Revise
- Revise the plan, answer questions, put detailed descriptions
- Make sure stored components are coherent and make sense
## 2.2 **🤖📋AI plan**: Generate tests
### Execute `/gen_tests`
### Revise
- Revise the tests, answer questions, put detailed descriptions
- Make sure stored tests are coherent and make sense
## 2.3 **🤖📋AI plan**: Generate Jira Epics
### Execute `/gen_epics`
### Revise
- Revise the epics, answer questions, put detailed descriptions
- Make sure epics are coherent and make sense
## 2.4 **🤖📋AI plan**: Component Decomposition To Features
### Execute
For each component in `docs/02_components` run
`/gen_features --component @docs/02_components/[##]_[component_name]/[component_name]_spec.md`
### Revise
- Revise the features, answer questions, put detailed descriptions
- Make sure features are coherent and make sense
# 3. Development phase
## 3.1 **🤖AI agent**: Feature implementation
For each component in `docs/02_components/[##]_[component_name]/` folder do next:
```
Read component description `@docs/02_components/[##]_[component_name]/spec.md`.
Read all features in the folder `@docs/02_components/[##]_[component_name]`. For each feature do next:
- Implement it
- Make sure feature is connected and communicated properly with other features and existing code
- Create unit tests from the Test cases description, run it and make sure the result is a success
- Create integration test for the feature, run and make sure the result is a success
If integration tests are specified in component spec, then write them and run, and make sure that component working correctly
```
## 3.2 **🤖AI agent**: Solution composition and integration tests
```
Read all the files here `docs/03_tests/` and for each file write down tests and run it.
Compose a final test results in a csv with the next format:
- Test filename
- Execution time
- Result
Fix all problems if tests failed until we got a successful result.
In case if one or more tests was failed due to missing data from user or API or other system, ask it from developer.
Repeat test cycle until no failed tests.
```
# 4. Refactoring phase