Models & Infrastructure
Model Training, Fine-Tuning & RAG
RAG, fine-tuning, or both: the right approach depends on the problem, not the hype.
RAG and fine-tuning solve different problems, and most vendors pitch whichever one they happen to sell. RAG fixes a knowledge gap: the model doesn't know your data. Fine-tuning fixes a behavior gap: the model knows the facts but won't consistently produce the tone, format, or judgment you need. This engagement starts by diagnosing which gap you actually have, then implements the retrieval pipeline, the fine-tuning, or both.
Sound familiar?
- ✓The model doesn't know your business, your data, or your tone, and generic prompting isn't fixing it
- ✓You're not sure whether you need RAG, fine-tuning, or both, and vendors keep pitching whichever they sell
- ✓A past fine-tuning attempt was expensive and didn't meaningfully improve outputs
What the engagement looks like
RAG vs. fine-tuning decision framework
A clear-eyed recommendation based on whether your problem is a knowledge gap, a behavior gap, or both.
RAG implementation
A retrieval pipeline built over your own documents and data, so answers are grounded and current.
Fine-tuning
Custom training on your data for consistent tone, format, or domain-specific behavior at scale.
This is a good fit if you're…
- Teams whose AI outputs are generic, inconsistent, or ungrounded in their own data
- Companies deciding between RAG and fine-tuning without a technical team to weigh in
- Anyone who tried fine-tuning once and wants a second, more targeted attempt
Rate for this engagement runs $150–$250/hr, scoped after a free intro call.
Frequently asked questions
How do I know if I need RAG or fine-tuning?
As a rule of thumb: if the model needs to know something specific to your business, that's RAG. If the model already knows the facts but produces the wrong tone, format, or judgment, that's fine-tuning. Many real cases need both, which the diagnostic step will clarify.
How much data do we need to fine-tune a model?
It varies by task, but useful results are often possible with a few hundred to a few thousand well-chosen examples. Quality and consistency of the examples matters more than raw volume.
Can this work with a proprietary model API, or only open-source models?
Both. Several proprietary providers support fine-tuning through their API, and RAG works with any model. The approach is chosen based on your existing setup, not a fixed preference for open or closed models.
Ready to get started with Model Training, Fine-Tuning & RAG?
No pitch deck, no obligation. Just a straight answer on whether AI can actually help your situation, and how.
$150–$250/hr · no contracts, no retainers required