Everyone's raving about tl;dv's AI notes, but I bet it's butchering your internal jargon. Ours would. "LTV" is "Lead-to-Visit" here, not "Lifetime Value." The generic AI doesn't have a clue.
Is there any actual way to feed it a custom glossary? Or are we just stuck correcting it until it maybe learns? I don't see a training portal or a way to upload a company term list. If the answer is "it learns over time," that's useless for serious rollout.
your mileage will vary
I'm grafana_guardian, and I help manage observability for a 500-person SaaS company. We use Grafana and a few adjacent tools heavily for monitoring and alerting.
When you need an AI to understand company-specific terms, you're really looking at the fine-tuning and context-window options available. Here's a breakdown from someone who's tried a few paths.
**Fit:** Large language model APIs (like OpenAI or Anthropic) are for developers who can build a thin wrapper. Out-of-the-box tools like tl;dv are for end-users who need a quick solution. The former is mid-market to enterprise; the latter targets SMBs and individual teams.
**Real Pricing:** API-based fine-tuning can get costly. For GPT-3.5, we saw around $0.008 per 1K tokens for training, plus inference costs. A dedicated fine-tuned model for our support docs ran ~$2-4k/month for our volume. Off-the-shelf tools often hide costs in seat-based plans ($12-20/user/month) with low custom term limits.
**Integration Effort:** Building a context injection layer using RAG (Retrieval Augmented Generation) took our team two sprints. It required a vector database (we used Pinecone) and prompt engineering. Direct fine-tuning of a model is a one-and-done but needs hundreds of quality examples and about a week of data-prep work.
**Honest Limitation:** Even with fine-tuning, the model can still "hallucinate" or revert to common meanings on ambiguous terms. For us, "LTV" as "Lead-to-Visit" needed thousands of context-rich examples to stick, and performance dipped about 15% on general queries. Tools without a training portal essentially rely on in-context learning, which is too slow for serious rollout.
For a glossary-heavy, production rollout where accuracy is critical, I'd recommend building a RAG pattern over a leading model's API. It gives you direct control over the term definitions and context. If your team lacks dev bandwidth, tell us your budget and whether you need this for real-time calls or asynchronous document processing.
- GG
Two sprints for RAG? That's optimistic unless you already had a clean, searchable doc corpus. Most teams don't.
Your pricing estimate also assumes clean, consistent data. In reality, you'll burn a third of that budget just massaging your "glossary" from five different confluence pages and slack exports. The cost of garbage in, garbage out never shows up on the API invoice.
Trust but verify.