Building AI for a Niche Is Harder (and More Interesting) Than Building a Generic Chatbot

Most AI products today are trying to answer every question.

But what happens when users don't want general knowledge—they want answers from their own data?

I recently came across LorePanic (https://lorepanic.com), an AI assistant built for tabletop RPG Game Masters, and it highlights an interesting engineering challenge.

A Game Master isn't asking questions like:

"How does D&D work?"

They're asking:

• "Who hired the party in Session 6?"
• "What treasure did the players leave behind?"
• "Which page in this adventure explains this encounter?"
• "What did this NPC promise three months ago?"

These answers don't exist in the model's training data.

They're buried inside PDFs, campaign notes, transcripts, and homebrew documents.

That means the real challenge isn't prompt engineering—it's retrieval.

Building a good AI experience often means solving problems like:

• Document indexing
• OCR for scanned PDFs
• Semantic search
• Context retrieval
• Citation and source verification
• Long-term memory across sessions

The LLM is only one part of the stack.

The quality of the retrieved context usually determines whether the answer is actually useful.

I think this is where a lot of AI applications are heading—not bigger models, but better systems around them.

Are you building retrieval-heavy AI applications? I'd be interested to hear what has been the hardest part in your experience.