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LLM Fine-Tuning & RAG

Turn general-purpose AI models into systems that answer reliably from your own knowledge.

LET'S TALK

Vertex Consulting designs systems built on large language models (LLMs) that answer from your approved knowledge. The core is often RAG, or retrieval-augmented generation: the system looks up relevant passages in your documents and gives them to the model with each question. We add fine-tuning where it helps, plus the testing, monitoring, and safety controls that real users need.

The problem this solves

Many enterprise LLM pilots fail for predictable reasons. The model does not know private business context, retrieval quality is uneven, prompts become brittle, and teams cannot explain why one answer is reliable while another is not. The gap between a chatbot demo and a production AI system is usually data engineering, evaluation, and operational discipline.

Fine-tuning and RAG solve different problems. Retrieval brings current, source-grounded context into the model. Fine-tuning retrains the model on examples to change its behavior, format, terminology, or task performance. Choosing the wrong tool creates cost, latency, and quality issues. Choosing both without evaluation creates a system that looks sophisticated but cannot be trusted.

How we build it

First, we agree on the task and the standard a good answer must meet. We define what good output means, what sources are authoritative, what failure modes are unacceptable, and what human review is required. Then we design the data pipeline: ingestion, cleaning, chunking, embeddings, metadata, access control, and retrieval ranking.

For fine-tuning, we assess whether training data is sufficient, whether the improvement can be measured, and whether a smaller or distilled model would reduce cost. We build evaluation sets before tuning so quality changes can be measured against real business tasks, not anecdotes.

Where it fits

Internal knowledge assistants grounded in policies, manuals, tickets, and technical documents.

Customer support copilots that cite approved sources and follow brand tone.

Document analysis workflows for contracts, reports, claims, or compliance evidence.

Domain-specific agents that produce structured outputs for downstream systems.

How the work is delivered

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We confirm the business case, test the riskiest technical assumptions, and build the smallest version that is useful in production. Then we prepare your team to own and run it.

What you get

A retrieval pipeline that finds the relevant passages in your approved documents and shows the source with each answer.

An evaluation set (real questions with agreed answers) that scores every prompt, model, or retrieval change before release.

Checks that flag answers no source supports, with a fallback or human review when the system is unsure.

A clear fine-tuning decision: tune only when tests show that retrieval and prompting are not enough.

Model choices matched to each task, including a smaller model where it passes the same tests at lower cost.

Frequently asked questions

Do we need fine-tuning or RAG?

Often RAG comes first. Fine-tuning is useful when the model needs a consistent behavior, style, format, or domain skill that prompting and retrieval cannot reliably produce.

How do you reduce hallucinations?

We combine source-grounded retrieval, answer constraints, citations, automated evaluations, fallback behavior, and human review for high-risk workflows.

Can private enterprise data stay protected?

Yes. The architecture can enforce document permissions, separate indexes, secure model access, logging controls, and provider choices aligned to your privacy requirements.

How is success measured?

We define task-specific evaluation sets with expected answers, citation expectations, format checks, and failure categories before production rollout.