Token-Efficient Research

If your team runs AI-assisted research workflows at any real volume, you have probably noticed the bill. This session is about where that cost actually comes from, how much of it is avoidable, and what a grounded, citation-accurate setup looks like in practice.

What we'll cover:

Why token costs are becoming a real budget problem

• What's shifted in the AI pricing landscape over the past year
• Why costs that looked negligible at low volume turn into a serious line item at scale
• Why research workflows are especially exposed: long contexts, repeated queries, citation-heavy outputs

Where your tokens are actually going

• A plain-language breakdown of token efficiency: what it means, what it costs when ignored, and where the waste hides
• The anatomy of a typical literature workflow and the token footprint at each step
• Common habits that quietly inflate your bill without improving your outputs
• Why token spend is only part of the picture

How to stop paying for tokens you don't need

This is the practical core of the session. We'll walk through concrete techniques you can apply immediately:

• Matching model size to task complexity: when a smaller model is the smarter choice
• Prompt patterns that return the same quality answer in fewer tokens
• What the Scite MCP is, how it works, and why it changes the cost calculation entirely
• Live demo: the same research question sent three ways: large model alone, mid-tier with grounding, small model with grounding. So you can see the tradeoffs in real time

No deep AI expertise required. If you're paying for tokens and wondering where the money goes, this session is for you.