RAG, fine-tune, or neither
The architecture decision that determines maintenance cost, accuracy ceiling, and vendor exposure for the next 24 months.
Generative AI consulting · Pilot challenge
Generative AI consultant.
Best fit when the question is whether the generative AI pilot is the right decision. Most generative AI consultants will sell you a pilot. Paul will tell you whether the pilot is the right decision — based on what is actually shipping inside his own companies and across the product portfolio Uvik serves.
When to hire
The generative AI decisions worth actually try to breaking.
Hired before the pilot is committed, when the cost of the wrong pilot is higher than the cost of pausing.
Model selection & switching
Closed model versus open weight, single provider versus multi-provider, where to commit and where to stay portable.
Eval & evidence discipline
Pre-deployment evaluation, golden datasets, hallucination guards, drift detection. The discipline that makes generative output defensible.
Internal vs. customer-facing
Where to deploy first. Internal systems with controlled blast radius first; customer-facing only after the eval discipline holds up.
Data governance & IP exposure
What data goes to the model, what stays internal, what the IP and confidentiality posture looks like under regulator and acquirer scrutiny.
Operator capacity & ROI
Who owns the system after launch. What the support model looks like. Where the ROI window actually is — not where the vendor pitch claims it is.
How it works
Pilot argue against, four-step.
Argue against the use case
Is generative AI the right tool for this problem, or is it the trendy tool? Honest answer in week one.
Architecture & vendor decision
RAG vs. fine-tune vs. agent. Closed vs. open. Single-provider vs. multi-provider. The choices that compound across 24 months.
Eval discipline before launch
Golden datasets, hallucination guards, exception escalation paths. The pre-launch evidence the system actually works on the company’s data.
Path to production
If the pilot succeeds, what does production look like? Operator owner, scale plan, governance posture. Pilot designed to graduate, not to demo.
Frequently asked
Common questions about this engagement.
What does a generative AI consultant actually do?
Stress-tests whether the proposed generative AI pilot is the right decision — before the architecture is committed. Most generative AI consultants will sell a pilot. Paul will tell the CEO whether the pilot is the right decision, based on AI agents actually shipping in production at Elogic Commerce and across Uvik Software's client portfolio.
Should I do a pilot or skip to production?
Most pilots fail because they're scoped to demo, not graduate. The right question is whether the use case has enough volume, exception predictability, and ROI window to justify production — and only then design a pilot scoped to validate that, with explicit graduation criteria written down before launch.
RAG, fine-tune, or agent — how do you choose?
Volume, refresh rate, accuracy ceiling, and maintenance cost determine the architecture. RAG when the underlying knowledge changes; fine-tune when the format and tone need to be locked in; agent when the system needs to take actions, not just generate text. The wrong choice is expensive to reverse.
How is data exposure managed?
Data governance is decided before architecture, not after. What data goes to the model provider, what stays in private infrastructure, what the IP and confidentiality posture looks like under acquirer or regulator scrutiny — settled at the start, documented, defensible.
What does success look like?
Success is defined by the operator-named KPI before launch, validated post-launch under The Proof Standard™: baseline established, intervention shipped, measurement window run, owner accountable. Not vendor-asserted ROI — client-validated outcome.
What does a generative AI consultant focus on that an AI consultant doesn’t?
A generative AI consultant focuses on the specific class of decisions that comes with foundation models: prompt and retrieval architecture, evaluation harness design, hallucination management, agent orchestration, content provenance, and the IP and content policy questions that traditional ML doesn’t face.
Discuss this engagement.
Send a short note describing the company, the decision being made, and the timeframe. First call within two business days.
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