Built an AI-powered eBook generator - looking for feedback on long-form LLM workflows

[Sam-May-Futurelab]

Hi everyone 👋

I’ve been experimenting a lot with long-form LLM workflows recently and ended up building Inkfluence AI - a tool that generates full eBooks, guides, and workbooks using AI (with chapter structure, long-form content, and custom cover design).

Since LLMware focuses heavily on structured workflows, context handling, and model orchestration, I thought this community might have some valuable insights.

🔗 Inkfluence AI

Would love feedback on:

• improving consistency across multi-chapter outputs,
• better ways to manage context across long sequences,
• strategies for reducing drift in long-form generation,
• any LLMware components that might pair well with this kind of tool.

Thanks for taking a look, any thoughts or suggestions are appreciated!

[KeepALifeUS]

Great project! Long-form LLM workflows for eBooks require careful orchestration. Here is feedback and patterns:

Key Challenges for Long-Form Generation

1. Context management: Books exceed context windows
2. Consistency: Characters, facts, style must be consistent
3. Quality control: Each section needs validation
4. Cost management: Long content = many API calls

Recommended Architecture

from dataclasses import dataclass, field
from typing import List, Dict

@dataclass
class BookState:
    # Structure
    outline: List[Dict] = field(default_factory=list)
    chapters: Dict[str, str] = field(default_factory=dict)
    
    # Consistency tracking
    characters: Dict[str, Dict] = field(default_factory=dict)
    plot_points: List[str] = field(default_factory=list)
    world_facts: Dict[str, str] = field(default_factory=dict)
    
    # Style guide
    tone: str = ""
    pov: str = ""
    tense: str = ""

class EBookGenerator:
    def __init__(self, llm_client):
        self.llm = llm_client
        self.state = BookState()
    
    async def generate_book(self, premise: str) -> str:
        # Phase 1: Planning
        self.state.outline = await self.generate_outline(premise)
        
        # Phase 2: World building
        self.state.characters = await self.develop_characters()
        self.state.world_facts = await self.build_world()
        
        # Phase 3: Chapter generation
        for chapter in self.state.outline:
            content = await self.generate_chapter(
                chapter,
                context=self.get_relevant_context(chapter)
            )
            self.state.chapters[chapter["id"]] = content
            
            # Validate consistency
            await self.validate_chapter(content)
        
        # Phase 4: Assembly
        return self.assemble_book()
    
    def get_relevant_context(self, chapter: Dict) -> str:
        """Build context from state - key for consistency"""
        relevant_chars = [c for c in self.state.characters.values()
                         if c["name"] in chapter.get("characters", [])]
        
        return f"""
Characters in this chapter:
{json.dumps(relevant_chars, indent=2)}

Established facts:
{json.dumps(self.state.world_facts, indent=2)}

Previous chapter summary:
{self.get_previous_summary(chapter)}

Style guide: {self.state.tone}, {self.state.pov}, {self.state.tense}
"""

Token Optimization

def compress_context(context: str, max_tokens: int) -> str:
    """Summarize context to fit token budget"""
    if count_tokens(context) <= max_tokens:
        return context
    
    # Summarize older content, keep recent verbatim
    return llm.summarize(context, target_tokens=max_tokens)

Consistency Validation

async def validate_chapter(self, content: str):
    # Check character consistency
    # Check plot continuity
    # Check factual consistency with world state
    pass

The key is treating state as the source of truth and always providing relevant context.

More patterns: https://github.com/KeepALifeUS/autonomous-agents