Fine-Tuning vs Prompting: When to Specialize an SLM

When the job is narrow and repetitive, you have ~1,000+ clean labeled examples, a named owner for evals and redeploys, enough volume that unit cost or latency hurts, and a prompted baseline you can beat on a held-out set. If any of those are missing, keep prompting (and add RAG if the job is knowledge).

Fine-tuning changes model weights for a specialist behavior. RAG retrieves trusted docs at ask-time so answers stay grounded and fresh. Private hosting is where inference runs. You can combine them in any order; picking VPC hosting does not mean you must fine-tune.

Usually one of: no held-out eval, labels that were raw tickets, an open-ended job that should not have been specialized, or no owner to keep the specialist from drifting. Specialize without a scoreboard is guessing.

Route traffic through an AI gateway. Set default models. Gate premium access. Expose per-team spend. TrueFoundry's AI Gateway gives engineering leads usage and cost visibility across 1,000+ LLMs behind one OpenAI-compatible API, so model choices do not pile into billing surprises while you are still proving the product.

Yes. TrueFoundry runs in your VPC, on-prem, air-gapped, or hybrid, so prompts and responses never leave your domain even as you route across many providers.