Make prompts more stuctured.

Separate tutorial.md for developers from commands for AI
WIP

docs/00_problem/1.3_07_assesment_prompt.md

@@ -1,10 +1,11 @@
-Read carefully about the problem:
+## The problem description
+   We have a lot of images taken from a wing-type UAV using a camera with at least Full HD resolution. Resolution of each photo could be up to 6200*4100 for the whole flight, but for other flights, it could be FullHD. Photos are taken and named consecutively within 100 meters of each other.
+   We know only the starting GPS coordinates. We need to determine the GPS of the centers of each image. And also the coordinates of the center of any object in these photos. We can use an external satellite provider for ground checks on the existing photos
+  
+## Data samples
+  Are in attachments: images and csv
 
- We have a lot of images taken from a wing-type UAV using a camera with at least Full HD resolution. Resolution of each photo could be up to 6200*4100 for the whole flight, but for other flights, it could be FullHD
-Photos are taken and named consecutively within 100 meters of each other.
-We know only the starting GPS coordinates. We need to determine the GPS of the centers of each image. And also the coordinates of the center of any object in these photos. We can use an external satellite provider for ground checks on the existing photos
- 
- System has next restrictions and conditions:
+## Restrictions for the input data
   - Photos are taken by only airplane type UAVs.
   - Photos are taken by the camera pointing downwards and fixed, but it is not autostabilized.
   - The flying range is restricted by the eastern and southern parts of Ukraine (To the left of the Dnipro River)
@@ -16,8 +17,8 @@ We know only the starting GPS coordinates. We need to determine the GPS of the c
   - Number of photos could be up to 3000, usually in the 500-1500 range
   - During the flight, UAVs can make sharp turns, so that the next photo may be absolutely different from the previous one (no same objects), but it is rather an exception than the rule
   - Processing is done on a stationary computer or laptop with NVidia GPU at least RTX2060, better 3070. (For the UAV solution Jetson Orin Nano would be used, but that is out of scope.)
-  
- Output of the system should address next acceptance criteria:
+
+## Acceptance criteria for the output of the system:
     - The system should find out the GPS of centers of 80% of the photos from the flight within an error of no more than 50 meters in comparison to the real GPS
 
     - The system should find out the GPS of centers of 60% of the photos from the flight within an error of no more than 20 meters in comparison to the real GPS
@@ -40,7 +41,7 @@ We know only the starting GPS coordinates. We need to determine the GPS of the c
 
     - The whole system should work as a background service. The interaction should be done by zeromq. Sevice should be up and running and awaiting for the initial input message. On the input message processing should started, and immediately after the first results system should provide them to the client
 
- Here is a solution draft:
+## Existing solution draft:
 
     # **ASTRAL-Next: A Resilient, GNSS-Denied Geo-Localization Architecture for Wing-Type UAVs in Complex Semantic Environments**
 
@@ -325,32 +326,37 @@ We know only the starting GPS coordinates. We need to determine the GPS of the c
     * Test_Performance: Run Test_Long_Route on min-spec RTX 2060. ASSERT average_time(Pose_N^{Est} output) < 5.0s (AC-7).  
     * Test_MRE: ASSERT TOH.final_MRE < 1.0 (AC-10).
 
-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.
+## Role
+  You are a professional software architect
 
-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:
+## Task
+  - Thorougly research in internet about the problem and identify all potential weak points and problems.
+  - Address these problems and find out ways to solve them.
+  - Based on your findings, form a new solution draft in the same format.
+  
+## Output format
+  - Put here all new findings, what was updated, replaced, or removed from the previous solution in the next table:
+   - Old component solution
+   - Weak point
+   - Solution (component's new solution)  
+  
+   - Form the new solution draft. 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. Put it 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
+     - Advantages of this solution. For example, LiteSAM AI feature is picked for UAV - Satellite matching finding, and it make its job perfectly in milliseconds timeframe. 
+     - Limitations of this solution. For example, LiteSAM AI feature matcher requires to work efficiently on RTX Gpus and since it is sparsed, the quality a bit lower than densed feature matcher.
+     - Requirements for this solution. For example, LiteSAM AI feature matcher requires that photos it comparing to be aligned by rotation with no more than 45 degree difference. This requires additional preparation step for pre-rotating either UAV either Satellite images in order to be aligned.
      - 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.
    
-Also, investigate these ideas: 
-- A Cross-View Geo-Localization Algorithm Using UAV Image
-https://www.mdpi.com/1424-8220/24/12/3719
-- Exploring the best way for UAV visual localization under Low-altitude Multi-view Observation condition 
- https://arxiv.org/pdf/2503.10692
-and find out more like this.
-
-Assess them and try to either integrate or replace some of the components in the current solution draft
+   
+## Additional sources 
+   - A Cross-View Geo-Localization Algorithm Using UAV Image
+      https://www.mdpi.com/1424-8220/24/12/3719
+   - Exploring the best way for UAV visual localization under Low-altitude Multi-view Observation condition 
+      https://arxiv.org/pdf/2503.10692
+   - find out more like this.
+   Assess them and try to either integrate or replace some of the components in the current solution draft