Let's talk about learning things the hard way. You know those moments when you're staring at a computer screen thinking, "Well, that wasn't supposed to happen"? While implementing AI-powered data enrichment for our use cases like account fit scoring and upsell analysis, I made some... let's call them "educational mistakes." Here's what I learned, so you don't have to learn it the expensive way.
1. The "Bigger is Better" Trap: A Tale of LLM Selection
Picture this: Me, bright-eyed and bushy-tailed, throwing our most powerful (and expensive) GPT model at EVERY. SINGLE. TASK. Because if it costs more, it must be better, right? nervous laughter
Spoiler alert: That wasn't my finest moment.
After watching our costs climb faster than my coffee intake, I discovered something interesting: our lighter-weight model (gpt-4o-mini) could handle most tasks just as well as its bigger, pricier sibling. Want some numbers? I recently processed 180,000 accounts for fit scores and 14,000 for engagement scores - total cost: $120. Our daily processing now runs about $5 in production. That's a fraction of what traditional ICP fit score tools would charge!
I now save the heavyweight gpt-4o for special occasions, like quarterly ICP analysis or drafting those extra-important customer communications. You know, the ones where you actually need the AI equivalent of Shakespeare.
2. From Testing Everything to Testing Smart
Here's where I made my biggest breakthrough: being selective about both testing and processing. Initially, I was like a kid in a candy store, enriching ALL THE THINGS! Now? I'm more like a careful shopper, and it starts way before production.
Let's talk testing strategy (because discovering token costs on your entire database is painful). I grab about 100 companies I know inside and out - from Fortune 500s to small startups. When Microsoft suddenly scores lower than a local food truck, I know we've got a problem. This targeted testing lets me:
- Iterate on prompts quickly without breaking the bank
- Catch obvious scoring issues before they hit production
- Extrapolate total costs before going big ("Oh, that would cost HOW much?")
Once I've got the prompts dialed in and costs looking reasonable, I stay picky about what goes into production:
- Active accounts actually doing things (no point scoring zombie accounts)
- Records with meaningful changes (20% employee growth? Yes. New favicon? No.)
- New accounts that need their first scoring
3. The Field That Almost Broke the Bank
Ever heard the phrase "death by a thousand cuts"? Well, I discovered its data enrichment equivalent: death by a thousand unnecessary re-processes. My near-financial-disaster came from including frequently changing fields like "last_enriched_date" in our prompts. Every time these fields updated (which was... constantly), our system would helpfully re-process the entire row.
Imagine leaving the tap running and coming back to find your water bill could fund a small island nation. Yeah, it was kind of like that.
4. Safety First (Because Lessons Were Learned)
After my adventure in "How to Speed-Run Your AI Budget," I implemented some guardrails that saved our accounting team from having a collective heart attack:
- Daily budget alerts at 50% & 100% thresholds
- Hard automatic shutoffs at 100% of monthly budget caps
- Restricted access to expensive models by environment - only prod leads can access gpt-4o
- Set up Census sync alerts for any job processing more than 1,000 rows in a day (our normal is ~100-200)
5. The "Keep It Simple" Monitoring System
Our solution for tracking issues? A good old-fashioned Google Sheet where our sales team logs "hmm, that's not right" moments. No fancy systems, no complicated processes - just straight feedback from the people using the data. Sometimes the best solutions are the ones that don't require a computer science degree to understand.
What's Next?
I've got my eyes on something promising that could drop our costs even further: prompt caching. It's basically a way to optimize how we structure our prompts to let OpenAI reuse parts of them. Early tests look good - I'll share more once we've got real numbers to back it up.
Remember: The goal isn't to use AI everywhere - it's to use it smartly. And sometimes, being smart means learning from someone else's mistakes. Like mine. You're welcome! 😉