Fine-tuning, RL, and what actually works.

Prompting and retrieval-augmented generation (RAG) are the right first step when the base model already has the capability and just needs context or instructions. Fine-tuning earns its place once you have a repetitive, high-volume workflow where you need consistent format and behavior, lower latency and cost, or accuracy the base model cannot reach through context alone. In practice the strongest systems combine all three: RAG supplies fresh facts, prompting sets the task, and fine-tuning bakes in the skill and style so you spend fewer tokens getting there.

SFT teaches a model to imitate labeled examples and is the fastest way to lock in format, tone, and known-correct behavior. RL optimizes against a reward signal instead of fixed labels, so the model can discover better strategies for goals that are easy to score but hard to demonstrate, such as passing a test suite or resolving a ticket. Modern post-training runs SFT first to establish a baseline, then applies RL (RLHF, RLAIF, or verifiable-reward methods like GRPO) to push quality beyond what demonstrations alone can reach.

RLVR replaces a learned human-preference model with an automatic, checkable reward: did the code compile, did the tests pass, did the answer match ground truth, did the tool call succeed. Because the reward is deterministic, training is cheaper and far less prone to reward hacking than preference-based RLHF. It fits agent workflows especially well, where success is often objectively measurable, and it is why PerceptEye scores every training iteration against a simulation that provides consistent ground truth.

Public leaderboards like MMLU, GPQA, and SWE-bench measure broad general capability, so large frontier models usually top them. On a narrow enterprise workflow, a well-tuned smaller model routinely matches or beats a frontier API on task-specific accuracy while running far cheaper and faster. The benchmark that matters is a held-out evaluation built from your own data and success criteria, which is why PerceptEye optimizes against a task-specific eval instead of generic leaderboard scores.

Generic accuracy is rarely enough. The metrics that predict production success are task completion rate on a representative held-out set, cost and latency per resolved task, calibration and refusal behavior on out-of-scope inputs, and regression against your previous model. Tie each of these to a business outcome - tickets resolved, documents processed, dollars saved - and gate every model promotion on the same stable evaluation so improvements are provable rather than anecdotal.

Less than most teams expect. A few hundred to a few thousand high-quality, representative examples is often enough for supervised fine-tuning, because coverage of edge cases matters more than raw volume. When labeled data is scarce, simulation and synthetic generation expand coverage and RL against a reward function reduces the need for hand-labeled outputs entirely. Start with the data you already have, measure against a real evaluation, and add targeted examples only where the model is weak.

A model frozen at ship date decays as data, tools, and workflows change. PerceptEye treats improvement as a continuous loop: production signals feed back into simulation and re-training, every candidate is scored against a stable evaluation before promotion, and prior capabilities are protected by mixing earlier tasks back into training and gating releases on regression checks, so the model adapts without silently losing skills it already had.

Calling frontier APIs directly for repetitive, knowledge-intensive workflows is expensive, slow, leaks data, and produces unpredictable quality. A private model trained with PerceptEye runs where you choose, keeps your data in your environment, and can match frontier-grade accuracy on your tasks at up to 100x lower inference cost.

Yes. The resulting private model and its weights belong to your team, and it can be deployed in your cloud, VPC, or on-premise so sensitive data never leaves your environment. PerceptEye is designed to slot into the serving stack you already run, which keeps you free of vendor lock-in and lets security and compliance teams retain full control.