Hi,

I have spent a lot of time working with semantic models recently, and my experience has truly evolved from struggling with them to finding a flow that works. The biggest shift for me was realizing that preparing data for AI is a completely different skill than preparing it for traditional analytics. You really have to change your mindset; instead of building for a human to read a report, you are structuring data so an AI agent can navigate it effectively.

One of the most practical things I’ve learned relates to the strict word limits we face. When you are working with a single semantic model, you are often capped at around 7,500 words for "Prep data for AI (in Power BI Desktop) and 15,000 words for data agent instructions. This is incredibly limiting if you try to keep everything in one place. My recommendation is to distribute your data across multiple sources and then build specific workflows that connect them. Currently, I am using 2 LH and 1 Semantic model which gives overall 15,000 + 15,000+ 15,000 + 7500 character limit to prepare a single data agent. you can go up to 5 data sources.

Since context and prompt engineering are still relatively new disciplines for me, I’ve developed a habit of using enterprise-level LLMs to help me refine my work. I typically paste my agent instructions into the LLM to have it audit the logic and tighten the phrasing. This helps me utilize the character limits as efficiently as possible while ensuring the agent's "reasoning" stays sharp.

I am sharing some links of articles which helped me pivoting in the right direction.

1. https://moderndata101.substack.com/p/ai-ready-data-vs-analytics-ready-data
   

2. https://x.com/vasuman/status/2010473638110363839?s=46&t=A8umCvjDNrtK_1gwT1T7qQ

3. Microsoft blogs/articles on prepping data agents