Last week we covered PayPal’s key move, put a smaller, fine tuned model into the Search and Discovery step to cut response time and operating cost, while keeping quality competitive on everyday questions (Sahami et al., 2025). This week we turn that insight into a practical sprint you can actually run, no data science department required.
What fine tuning means in business terms
You teach a compact model how your customers ask and how you answer, so it recognizes budget, timeline, location, specs, or service type and instantly returns the right options. This mirrors PayPal’s workflow, a specialized model for the hot path, a larger model only when questions are unusual or complex (Sahami et al., 2025).
The 10 Day Plan, copy this
Day 1 to 2, pull the right source material
- Top 200 customer questions from chat, email, SMS, or call notes
- Your product or service list with key attributes, price, availability, location, turnaround, warranty
- Ten examples of great answers from your best reps, short and specific and tied to an action such as book, buy, schedule
This becomes your training set. Keep it clean and on brand (Sahami et al., 2025).
Day 3, create simple training pairs
For each question, add three fields:
- Extracted attributes, for example budget 100 to 150 dollars, need waterproof, size medium, location Toronto
- The internal search translation, what you would type into your system to find the match
- A concise response, two to four options plus one clear next step
This mirrors the Search and Discovery sequence, understand to translate to respond, that PayPal optimized (Sahami et al., 2025).
Day 4 to 5, teach the small model the pattern
Feed the model 200 to 500 of these pairs. Emphasize:
- Attribute extraction such as budget, spec, timeline
- One pass search translation, no rambling
- Short, confident responses with a single next step
This is how you convert smart into fast (Sahami et al., 2025).
Day 6, build confidence checks
If the question is vague or the model is unsure, ask one clarifying question before answering. If still unsure, hand off to a human. This keeps accuracy high without slowing the hot path (Sahami et al., 2025).
Day 7 to 8, A/B test on revenue pages
Route a slice of traffic so the small model handles Search and Discovery. Keep your current setup as control. Track:
- Time to first suggestion
- Conversion from chat such as booked call, quote request, add to cart or checkout
- Cost per conversation
This is the show me the business impact moment (Sahami et al., 2025).
Day 9, tighten where it stumbled
Add 30 to 50 new examples taken from misses in the A/B test, for example misunderstood budget, wrong category, weak follow up. Fine tune again. Small, frequent updates are an advantage of compact models (Sahami et al., 2025).
Day 10, roll out with guardrails
Make the small model your default for Search and Discovery. Keep the larger model for complex comparisons or unusual, multi step requests. Optional, enable advanced retrieval only for ambiguous, long tail queries so you avoid latency overhead on the main path (Sahami et al., 2025).
What good looks like after 10 days
- First suggestions arrive in about one to two seconds, customers stay engaged
- Responses are concise and on brand, two to four strong options and a single, obvious next step
- Fewer escalations to humans for routine questions, your team focuses on high value cases
- Cost per conversation trends down because the smaller model handles the hot path (Sahami et al., 2025)
Owner checklist, quick review
- Do we have a clean list of top questions and top margin drivers
- Are our answers short, specific, and action oriented
- Did we measure time to first suggestion and cost per conversation before testing
- Did we set a confidence threshold and a single clarifying question
- Are we escalating to the larger model only when the use case truly needs it
Common pitfalls and how to avoid them
- Too much narration, train the assistant to extract, translate, answer
- No confidence check, one clarifying question prevents most misfires
- All or nothing rollout, start with a slice of traffic and let data drive rollout
- Using advanced retrieval everywhere, save it for ambiguous tail queries and keep the main path lean (Sahami et al., 2025)
Interested?
Comment DEMO and I will send a 90 second video of our AI Receptionist handling a real inquiry, greeting the lead, asking one smart clarifying question, presenting tailored options, and booking the appointment, so you can judge the pace and flow for your business.
Sahami, A., Garg, S., Wang, A., Kulkarni, C., Farahani, F., Chuang, S. Y.-S., Wan, J., Manoharan, S., Kona, U., Sharma, N., Pang, L., Mehrotra, P., Clark, J., & Moyou, M. (2025). NEMO-4-PAYPAL: Leveraging NVIDIA’s NeMo Framework for empowering PayPal’s commerce agent (arXiv:2512.21578). arXiv. https://doi.org/10.48550/arXiv.2512.21578
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