The Two Paths to Custom AI

When you want AI that knows your stuff, you have two main options:

RAG (Retrieval-Augmented Generation) — Feed context at runtime

Fine-tuning — Train the model on your data

Let's break down each.

RAG: The Quick-Start Approach

How It Works

Split your documents into chunks

Embed them into vectors

At runtime, retrieve relevant chunks

Feed them to the LLM as context

Pros

Fast to implement (days, not weeks)

Can update knowledge instantly

No training costs

Works with any LLM

Cons

Context window limits apply

Retrieval quality = output quality

Can be slower (extra API call)

Best For

FAQ bots

Document Q&A

Knowledge base assistants

Anything needing current data

Fine-Tuning: The Deep Customization

How It Works

Gather training data (prompts + responses)

Train a base model on your data

Deploy the fine-tuned model

Pros

Model "knows" your style natively

Faster inference (no retrieval)

Can learn complex patterns

Works without context

Cons

Expensive (training costs + hosting)

Slow to update (retrain required)

Needs lots of quality data

Overfitting risk

The Hybrid Approach (Our Recommendation)

Start with RAG — Get something working fast

Add fine-tuning later — Once you have data and know what matters

Use both — Fine-tuned model for core capability, RAG for up-to-date info

Our Take

For 90% of SMEs: Start with RAG.

It's faster, cheaper, and easier to maintain. Fine-tune only when you have:

Clear ROI from customization

Enough data (100s-1000s of examples)

Need for sub-second responses at scale

RAG vs Fine-Tuning: When to Use Each

The Two Paths to Custom AI

RAG: The Quick-Start Approach

How It Works

Pros

Cons

Best For

Fine-Tuning: The Deep Customization

How It Works

Pros

Cons

The Hybrid Approach (Our Recommendation)

Our Take

Ready to put this into practice?

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