The Reality
While tools like Elicit and Consensus are emerging, building a complete system exposed unexpected complexity. The hardest parts weren't AI reasoning, but orchestration for real-world standards:
- Synthesis vs. Summarization: True synthesis requires understanding relationships between ideas, not just gathering information
- Quality Control: Academic standards demand perfect formatting—AI make systematic errors
- Integration: Combining working components into reliable pipelines is surprisingly difficult
Key Insights
Specialized agents work better than monolithic approaches
Multiple validation layers are essential
Personal solutions outperform one-size-fits-all tools
I documented this journey in an 8-part series covering everything from architectural decisions to citation integrity. The goal isn't prescriptive solutions, but illuminating challenges you'll face building systems that meet professional standards.
Whether automating literature reviews or technical documentation, understanding these complexities is crucial.
https://reckoning.dev/series/aireviewwriter
TL;DR: Built AI for publication-quality review articles. AI reasoning was easy—professional standards were hard.