# Rag Vs Fine-Tuning For Document Qa 2024

> Source: <https://dev.to/samchenreviews/rag-vs-fine-tuning-for-document-qa-2024-4bpc>
> Published: 2026-06-12 16:22:03+00:00

Hey Build Log listeners, it’s Nick. If you’ve ever stared at an invoice for a custom‑trained LLM and thought, “Did I just pay a premium for yesterday’s data?”, you’re not alone. Over the past three months I ran a head‑to‑head test between a fine‑tuned OpenAI model and a Retrieval‑Augmented Generation (RAG) stack built on the same data set. The result? A clear, cheap, and *future‑proof* winner.

In this post I’ll walk you through the exact experiments I ran, break down the economics, give you a step‑by‑step guide to building a production‑grade RAG pipeline, and hand you a cheat sheet for deciding when (if ever) fine‑tuning still makes sense. No fluff, just actionable tips you can copy‑paste into your next sprint.

Put those three together and you have a perfect storm: you’re paying more for a stale brain while the cheaper compute you need to keep it up‑to‑date is sitting idle.

Here’s the bottom‑line cost breakdown from my three‑month trial (all figures rounded):

```
  Item
  Fine‑Tuned Model
  RAG (GPT‑4o + Vector Store)

  Initial training (tokens)
  $2,200
  $0

  Monthly inference (10 k calls)
  $1,800
  $480 (GPT‑4o) + $60 (vector ops)

  Data refresh (quarterly)
  $1,200 (re‑train)
  $30 (new embeddings)

  Total 3‑month cost
  **$5,200**
  **$1,620**
```

That’s a **3.2× ROI** in favor of RAG. The numbers don’t lie, but the story behind them matters just as much.

My test cohort consisted of 150 internal engineers and product managers who asked a total of 12 k questions over 90 days. Here’s what the data showed:

Yes, the fine‑tuned model was a hair faster, but the latency difference is invisible to a human when you factor in the *time saved* from fewer corrections.

Below is the exact recipe I used. Feel free to swap out components (e.g., Milvus for Pinecone) – the pattern stays the same.

**Ingest & Chunk Your Docs**

Pull source files from your CMS, Git repo, or Confluence export.

**Embed with the Latest Model**

OpenAI text-embedding-3-large or e5‑large-v2 (open‑source) for best price/performance.

**Populate a Vector Store**

I chose [Pinecone](https://www.pinecone.io) for its automatic scaling and TTL support.

**Retrieve + Rerank**

Top‑k = 12. Pass the results to a lightweight cross‑encoder (e.g., sentence‑transformers/all‑mpnet‑base‑v2) for a second‑stage rerank.

**Prompt & Guardrails**

System prompt example:

You are an internal knowledge‑base assistant for *Acme Corp*. Use ONLY the provided excerpts. If the answer is not found, say “I don’t have that info yet.”

```
  - Wrap the retrieved chunks in a &lt;context&gt; block and feed them to gpt‑4o‑preview (or your chosen LLM).
```

**Quick tip:** Set up a CI/CD job that runs the ingest‑embed‑store workflow nightly. That way any new markdown file is searchable within 30 seconds of commit.

Fine‑tuning isn’t dead; it just needs a very narrow justification. Use this checklist before you spend a single dollar on a custom model:

If you answered “no” to all of those, skip the fine‑tune and double down on RAG.

```
  Scenario
  RAG
  Fine‑Tune

Docs change weekly
  ✅ Auto‑ingest &amp; re‑embed
  ❌ Need full retrain

Heavy brand‑voice compliance
  ⚠️ Must add post‑processing guardrails
  ✅ Model internalizes style

Budget‑constrained startup
  💰 Lower OPEX
  💸 High upfront CAPEX

Sub‑second latency SLA
  🕒 Add ~200 ms vector fetch
  ⚡ Pure LLM call
```

Copy this into a spreadsheet and plug in your numbers:

EmbeddingCost = (TotalChunks * TokensPerChunk * EmbeddingRate) / 1_000_000

InferenceCost = (MonthlyCalls * AvgTokensPrompt * LLMRate) / 1_000_000

VectorLookupCost = (MonthlyCalls * LookupOps * LookupRate)

TrainingCost = (TrainingTokens * FTRate) / 1_000_000

InferenceCostFT = (MonthlyCalls * AvgTokensPrompt * FTModelRate) / 1_000_000

RefreshCost = (RefreshTokens * FTRate) / 1_000_000 # only if you re‑train

Replace EmbeddingRate, LMR ate, etc., with the latest pricing from your provider. In my case:

The spreadsheet will instantly show you the break‑even point – usually around 5 k monthly queries for most mid‑size enterprises.

If you found this post helpful, grab a coffee and hit the **Subscribe** button on your favorite podcast platform. New episodes drop every Tuesday, and I’ll keep digging into the gritty, money‑talking side of AI that most blogs gloss over.

*Adapted from an episode of Signal Notes. Listen on your favorite podcast app.*