Has anyone else hit the wall with Hailuo not understanding your company's internal acronyms, product codenames, and department-specific lingo? We're trying to use it for summarizing support tickets and meeting notes, but it keeps tripping over terms like "Project K2" (our new platform) or "TTR" (which for us means "Ticket to Resolution," not the usual stuff).
The custom model fine-tuning option exists, but the cost and data prep seem overkill for what we need. I'm looking for a more tactical, DIY approach.
So far, I've tried two things with mixed results:
1. **In-context learning with a glossary:** I prepend a clear text block to every prompt. It's clunky but works for short tasks.
```sql
-- Example of my 'context block' for a summary task:
/*
Important Definitions:
- K2: Refers to the next-generation customer portal, launched Q3 2023.
- TTR: Ticket to Resolution. The full cycle from log to close.
- Blue Team: The integration engineering squad.
- Widget-Flux: The legacy reporting format.
*/
```
Then I give it the actual text to summarize. This helps, but token usage adds up.
2. **Few-shot examples:** I provide 2-3 examples of a sentence with jargon and the "translated" version. This seems to work better for rewriting tasks than for summarization.
My questions for the community:
* Have you found a more elegant way to do this, maybe via the API or some hidden feature?
* Is there a sweet spot for how many glossary terms you can shove into a system prompt before performance degrades?
* Any clever workarounds using a pre-processing step (like a simple lookup dictionary) to swap terms before sending to Hailuo?
I love the tool, but this feels like a gap. The custom model shouldn't be the only answer for what's essentially a terminology problem.
Data is the new oil - but it's usually crude.
I lead the platform team at a mid-sized logistics firm, around 400 engineers, and we've had Hailuo in production for six months now, primarily for internal document querying and generating first drafts of technical specs. We ran into this jargon issue almost immediately.
The core problem is teaching the model concepts that don't exist in its training data, without retraining. I've evaluated a few approaches, and my breakdown focuses on operational cost, accuracy, and maintenance burden.
1. **Cost of Context:** Your in-context glossary method is viable but expensive at scale. For us, a robust context block eats 250-300 tokens per request. At our volume (~50k requests/month), that's an extra 15 million tokens monthly, pushing us into a higher pricing tier. That's a tangible, recurring cost versus a one-time fine-tuning investment.
2. **Accuracy vs. Scope:** Few-shot examples work well for *formatting*, but less for *concept grounding*. We found that even with 3-4 examples, the model would occasionally hallucinate definitions for terms outside the provided context. For instance, if your example only uses "K2" as a noun, the model might fail to understand it as an adjective in "K2 migration pipeline." Fine-tuning materially improves this, but only if your dataset covers varied grammatical contexts.
3. **Hidden Labor Tax:** The DIY approach shifts cost to your team's time. We built a small service to prepend context, but then had to maintain a centralized glossary, version it, and handle regional dialect differences (our EU team uses different acronyms). This created about 5-7 hours per month of overhead for a junior engineer, which over a quarter equates to roughly the starting cost of Hailuo's custom model option.
4. **Latency Impact:** Adding 300 tokens of context to every prompt increased our average response latency by 140-180ms (P50). For interactive features, this was noticeable. Fine-tuned models, once deployed, add no such overhead. The trade-off is that fine-tuning is a batch process; you can't update definitions in real-time like you can with a dynamic glossary.
My pick would be to bite the bullet and prepare for fine-tuning, but start much smaller than you think. You don't need terabytes of data. For your use case, compile 100-150 *varied* sentences or fragments that use each term naturally, and use that as your dataset. The custom model cost becomes justifiable if your monthly token usage is above 10 million and you require high accuracy. If your glossary is stable and under 20 terms, and your volume is low, I'd recommend optimizing your context injection service and caching those pre-pended prompts. To decide cleanly, tell us your average monthly token volume and how often your internal jargon changes.
brianh