Gyld vs fine-tuning
External, fresh, permissioned knowledge vs. knowledge baked into model weights
What is fine-tuning?
Fine-tuning adjusts a model's weights by training it on your data, so the behavior or knowledge becomes part of the model itself. It is effective for teaching style, format, or narrow tasks, but it bakes information into weights — which makes it expensive to update, hard to attribute to a source, and impossible to permission per user.
Gyld vs fine-tuning: how they compare
Fine-tuning and a context layer answer different questions. Fine-tuning changes how a model behaves; a context layer changes what a model knows at query time. For company knowledge that changes daily and must respect who can see what, Gyld keeps the knowledge external, current, source-cited, and access-controlled — without retraining anything.
| Gyld | fine-tuning | |
|---|---|---|
| Where knowledge lives | External knowledge base, retrieved at query time | Baked into model weights |
| Freshness | Updates as your apps change | Stale until you retrain |
| Source citations | Agents can cite where a fact came from | No attribution |
| Access control | Per-user visibility enforced at retrieval | Not possible — weights are shared |
| Cost to update | Re-index (cheap, continuous) | Re-train (expensive, periodic) |
| Best at | Knowing your company facts | Teaching style, tone, or narrow tasks |
When to choose Gyld
- Your knowledge changes often and must stay current
- You need source citations and per-user permissions
- You want any agent to use the knowledge, not one fine-tuned model
When to choose fine-tuning
- You need to teach a model a specific output style, format, or domain skill
- You have a stable, narrow task where behavior matters more than facts
- You are optimizing latency/cost for a single specialized model in production
Frequently asked questions
Should I fine-tune a model or use a context layer?
Use fine-tuning to change how a model writes or performs a narrow task. Use a context layer like Gyld to give the model current, source-cited company facts that respect permissions. Many teams do both — fine-tune for style, use Gyld for knowledge.
Why not just fine-tune on our company data?
Company data changes constantly and has access rules. Fine-tuning freezes it into weights, cannot cite sources, and cannot restrict what one user sees. Gyld keeps knowledge external so it stays fresh, attributable, and permissioned.
Give your agents real company context
Gyld is the business context layer for AI — connect your apps, build your company brain, and plug it into any agent over MCP.