wiki/appdoc/doc_system_prompt_flow.md

Prompt Building Flow - Summary

✅ Current State: CLEAN AND SIMPLE

No placeholders - Everything handled through function parameters.

---

Complete Flow

1. mainServiceAi.callAiDocuments()
   └─> Delegates to subCoreAi.callAiDocuments()

2. subCoreAi.callAiDocuments()
   ├─> Builds FIRST prompt: buildGenerationPrompt(..., continuationContext=None)
   └─> Calls _callAiWithLooping(prompt, ..., promptBuilder, promptArgs)

3. _callAiWithLooping() - Loops until complete
   ├─> Iteration 1: Uses prompt from step 2
   └─> Iteration 2+: 
       ├─> Builds continuationContext = buildContinuationContext(allSections, lastRawResponse)
       └─> Calls promptBuilder(**promptArgs, continuationContext=continuationContext)

4. buildGenerationPrompt()
   ├─> If continuationContext has valid last_raw_json: Build CONTINUATION prompt
   └─> Else: Build INITIAL prompt

5. buildContinuationContext()
   └─> Returns: {section_count, last_raw_json}  [Simplified - only what's needed]

---

Key Files & Responsibilities

mainServiceAi.py

• Role: Entry point, delegation only
• Responsibility: Initialize AI objects, delegate to subCoreAi

subCoreAi.callAiDocuments()

• Role: Document generation coordinator
• Responsibility:
  • Build first prompt
  • Prepare promptArgs for continuation
  • Call looping function

subCoreAi._callAiWithLooping()

• Role: Iteration manager
• Responsibility:
  • Manage iterations
  • Build continuation context for iterations 2+
  • Call prompt builder with context
  • Extract sections and detect completion

subPromptBuilderGeneration.buildGenerationPrompt()

• Role: Prompt builder
• Responsibility:
  • Build initial prompt (if continuationContext=None or invalid)
  • Build continuation prompt (if continuationContext has valid last_raw_json)
  • Handle both cases in one function

jsonUtils.buildContinuationContext()

• Role: Context builder
• Responsibility:
  • Extract section_count from accumulated sections
  • Extract and clean last_raw_json from last response
  • Return minimal context dict

---

Data Flow

Initial Call:
  userPrompt, title, extracted_content
    ↓
  buildGenerationPrompt(..., continuationContext=None)
    ↓
  Initial Prompt (no continuation)

Continuation Call:
  allSections, lastRawResponse
    ↓
  buildContinuationContext(allSections, lastRawResponse)
    ↓
  {section_count: N, last_raw_json: "..."}
    ↓
  buildGenerationPrompt(..., continuationContext={...})
    ↓
  Continuation Prompt (with last JSON fragment)

---

Improvements Made

1. ✅ Simplified buildContinuationContext()

   • Removed unused fields: last_content_sample, last_content_type, continuation_guidance
   • Only returns: section_count, last_raw_json

2. ✅ Improved empty JSON handling

   • Validates last_raw_json is not empty or {}
   • Falls back to initial prompt if invalid

3. ✅ Clearer continuation check

   • Single boolean has_continuation checks all conditions
   • Eliminates nested if/else confusion

---

No Placeholders Needed!

The old placeholder-based approach (<CONTINUATION_INSTRUCTION>) has been completely removed. All logic is handled through:

• Function parameters (continuationContext)
• Direct conditional logic in buildGenerationPrompt()

This is much simpler and easier to understand and maintain.

---

✅ Generic for Any User Prompt

Yes, this logic works for ANY user prompt!

Why It's Generic:

1. No Content Assumptions

   • userPrompt is inserted directly into prompts without parsing or validation
   • No restrictions on length, format, or content type
   • No language detection required - AI handles any language

2. Flexible JSON Template

   • Supports multiple content types: heading, paragraph, table, list, code
   • AI chooses appropriate content_type based on the request
   • Can combine multiple content types in one document

3. Generic Continuation

   • Shows the exact last JSON fragment (works with any content)
   • No assumptions about what was generated
   • AI continues from wherever the fragment stopped

4. No Special Handling Required

   • Works for:
     • Lists (like "Generate 9000 prime numbers")
     • Text content (like "Write a story about...")
     • Structured data (like "Create a report with tables")
     • Code (like "Generate Python code for...")
     • Mixed content (like "Create a document with headings, paragraphs, and tables")

Example Prompts That Work:

# Numbers/Data
"Generate the first 9000 prime numbers"
"List all countries and their capitals"
"Create a multiplication table from 1 to 100"

# Text Content  
"Write a comprehensive guide about Python programming"
"Summarize the history of artificial intelligence"
"Create a document about project management best practices"

# Structured Data
"Create a report with sales data in tables"
"Generate a comparison chart of programming languages"
"Build a document with headings for each section"

# Code
"Generate Python code examples for common algorithms"
"Create documentation with code snippets"
"Write a tutorial with code blocks"

# Mixed Content
"Create a technical document with headings, paragraphs, lists, and code examples"
"Generate a report with introduction, tables, charts, and conclusion"

Practical Considerations:

1. Token Limits: Very long prompts might hit model token limits, but this is handled by the continuation mechanism
2. Special Characters: Any characters in the prompt are passed through (AI handles them)
3. Multi-line Prompts: Fully supported - inserted into f-strings as-is
4. Language: Works with any language - AI detects and responds appropriately

The Logic Adapts:

• Initial Prompt: Tells AI to generate content based on user request
• Continuation Prompt: Shows last fragment and asks to continue
• Completion Detection: AI sets complete_response: true when done

The AI model does the adaptation, not the code. The code just provides a consistent structure and continuation mechanism.