LLM Fine Turning Method

1 Method......................................................................................................................................... 1

1.1 Prompt Engineering (No Training)........................................................................................ 6

1.2 Retrieval-Augmented Generation (RAG)............................................................................... 7

1.3 Prompt-Tuning / Soft-Prompting........................................................................................... 8

1.4 LoRA ? QLoRA Fine-Tuning.................................................................................................... 8

1.5 Full Fine-Turning (Updating All Weights).............................................................................. 9

1.6 Pretraining a Domain-Specific Model (Most Expensive)...................................................... 9

1 Method

LLM Customization Landscape — Comparison Table

Method	What It Is	Best For	Pros	Cons	Cost	Data Needed	Skill Level
1. Prompt Engineering	Designing structured prompts, templates, system instructions	Customer support, sales workflows, simple logic, multi‑agent orchestration	Free, fast, zero risk, works with any model	Limited control, no domain reasoning	$0	None	Beginner
2. RAG (Retrieval‑Augmented Generation)	Vector DB + embeddings to “look up” business knowledge	Policies, product catalogs, internal docs, knowledge bases, ERP/BI	No training, fresh data, scalable, model‑agnostic	Doesn’t teach new skills, only improves factual grounding	Low	Medium (documents)	Intermediate
3. Prompt‑Tuning / Soft‑Prompting	Training small adapters that steer model behavior	Tone/style alignment, customer voice, lightweight domain adaptation	Cheap, fast, reversible	Limited depth, no reasoning improvement	Low	Small curated dataset	Intermediate
4. LoRA / QLoRA Fine‑Tuning	Training low‑rank matrices added to the model	Domain reasoning, workflows, multi‑step logic, coding agents, ERP/AI/BI	Huge impact, affordable, runs on home GPUs	Needs clean data, risk of overfitting	Medium	Medium–large high‑quality dataset	Intermediate–Advanced
5. Full Fine‑Tuning	Updating all model weights	Proprietary agent systems, specialized industries, deep reasoning	Maximum control, best performance	Very expensive, large GPUs, maintenance burden, catastrophic forgetting	High	Large curated dataset	Advanced
6. Domain‑Specific Pretraining	Training a foundation model from scratch or near‑scratch	Bloomberg‑style models, Med‑PaLM, defense tech	Absolute control, can outperform general LLMs	Millions of dollars, months of training, research team required	Very High	Billions of tokens	Expert / Research Team

General Business Comparison Table

Business Need	Best Method	Why It Works
Use company knowledge (policies, documents, product info)	RAG (Retrieval‑Augmented Generation)	Keeps information up‑to‑date, no model training required, easy to maintain
Make the AI understand your industry or workflows	LoRA / QLoRA Fine‑Tuning	Strong improvement in reasoning for low cost; efficient for most businesses
Control how the AI behaves in workflows or processes	Prompt Engineering	Fastest and cheapest way to shape AI behavior; no training needed
Match your company’s tone, voice, or brand style	Soft‑Prompting / Prompt‑Tuning	Lightweight customization that adjusts style without heavy training
Build a deeply customized internal AI system	Full Fine‑Tuning (optional)	Gives maximum control when your business needs unique internal reasoning
Create your own proprietary AI model	Pretraining	Only for large enterprises with major budgets; used when full ownership is required

1.1 Prompt Engineering (No Training)

What it is: You design structured prompts, templates, and system instructions.

Best for:

· Customer support scripts

· Sales workflows

· Simple business logic

· Multi‑agent orchestration (your specialty)

· Pros:

· Free

· Fast

· Zero risk

· Works with any model

· Cons:

· Limited control

· Not enough for domain‑specific reasoning

1.2 Retrieval-Augmented Generation (RAG)

What it is: You keep your business data in a vector database and let the model “look up” facts instead of memorizing them.

Best for:

Policies

· Product catalogs

· Internal documents

· Knowledge bases

· ERP/BI data (fits your enterprise background)

· Pros:

No model training

· Data stays fresh

· Highly scalable

· Works with small or large models

· Cons:

· Doesn’t teach the model new skills

· Only improves factual grounding

1.3 Prompt-Tuning / Soft-Prompting

What it is: You train a small “adapter” that modifies the model’s behavior without touching the base weights.

Best for:

· Tone/style alignment

· Customer‑facing voice

· Lightweight domain adaptation

Pros:

· Very cheap

· Fast to train

· Reversible

Cons:

· Limited depth

· Doesn’t fix reasoning gaps

1.4 LoRA ? QLoRA Fine-Tuning

What it is: You train small low‑rank matrices that plug into the model.

Best for:

· Domain‑specific reasoning

· Industry‑specific workflows

· Multi‑step logic

· Coding agents

· ERP/AI/BI tasks (your wheelhouse)

Pros:

· Huge impact

· Cheap compared to full fine‑tuning

· Works on your home lab GPUs

Cons:

· Needs clean, high‑quality data

· Can overfit if done poorly

1.5 Full Fine-Turning (Updating All Weights)

What it is: You retrain the entire model on your business data.

Best for:

· Proprietary agent systems

· Highly specialized industries

· When you need the model to think like your company

Pros:

· Maximum control

· Best performance

Cons:

· Very expensive

· Requires large GPUs

· Hard to maintain

· Risk of catastrophic forgetting

1.6 Pretraining a Domain-Specific Model (Most Expensive)

What it is: You start from scratch or from a foundation model and train on billions of tokens.

Best for:

· Companies like Bloomberg, Med‑PaLM, or defense tech

· When you need a proprietary foundation model

Pros:

· Absolute control

· Can outperform general LLMs

Cons:

· Millions of dollars

· Months of training

· Requires a research team