How to Train AI on Your Own Data (3 Methods)

Out-of-the-box AI models know a lot about the world but nothing about your business. They cannot answer questions about your internal processes, your product documentation, or your customer data. To make AI genuinely useful, you need to train AI on your own data.

There are three practical methods to do this, each with different tradeoffs in cost, complexity, and capability. This guide explains all three, with enough detail to help you pick the right one and get started.

The Three Methods at a Glance

Most businesses should start with RAG. It is the fastest to implement, the cheapest to run, and the easiest to update when your data changes.

Method 1: RAG (Retrieval-Augmented Generation)

RAG does not actually change the AI model. Instead, it retrieves relevant information from your data and includes it in the prompt. Think of it as giving the AI a cheat sheet before every answer.

How RAG Works

1. Chunk: Your documents (PDFs, web pages, database records) are split into smaller pieces, typically 200-500 words each.
2. Embed: Each chunk is converted into a numerical vector (an embedding) that captures its meaning.
3. Store: Vectors are saved in a vector database for fast similarity search.
4. Retrieve: When a user asks a question, the question is also embedded, and the most similar document chunks are retrieved.
5. Generate: The retrieved chunks are added to the AI's prompt as context, and the model generates an answer based on that context.

User Question → Embed → Search Vector DB → Top 5 Chunks →
Prompt: "Using this context: [chunks], answer: [question]" → AI Response

RAG Frameworks

Two frameworks dominate the RAG landscape in 2026:

LangChain excels at orchestrating complex multi-step AI workflows. It provides modular components for document loading, text splitting, embedding, vector storage, and retrieval. LangGraph, its companion library, adds workflow control for tasks that require multiple reasoning steps. LangChain is open-source under the MIT license.

LlamaIndex focuses specifically on document indexing and retrieval. In 2025, it achieved a 35% boost in retrieval accuracy, making it the top choice for document-heavy applications. LlamaIndex offers a simpler API when your primary goal is connecting AI to your data.

Many production systems use both: LlamaIndex for ingestion and indexing, LangChain for orchestration and output formatting.

Building a Basic RAG Pipeline

from langchain.document_loaders import DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA

# 1. Load your documents
loader = DirectoryLoader("./company_docs/", glob="**/*.pdf")
documents = loader.load()

# 2. Split into chunks
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = splitter.split_documents(documents)

# 3. Create embeddings and store
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(chunks, embeddings, persist_directory="./db")

# 4. Build the QA chain
llm = ChatOpenAI(model="gpt-4o-mini")
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=vectorstore.as_retriever(search_kwargs={"k": 5}),
)

# 5. Ask questions
answer = qa_chain.invoke("What is our refund policy for enterprise customers?")

RAG Costs

RAG is the cheapest approach. OpenAI's embedding model (text-embedding-3-small) costs $0.02 per million tokens. Embedding a 100-page document costs fractions of a cent. The main ongoing cost is the LLM calls for generating answers, which run $0.15 per million input tokens with GPT-4o-mini.

Vector databases like Chroma and FAISS are free and open-source. Hosted options (Pinecone, Weaviate Cloud) have free tiers for small projects.

When RAG Falls Short

RAG has limits. It works when the answer exists somewhere in your documents. It does not work when you need the AI to learn a new skill, adopt a specific writing style, or understand implicit domain knowledge that is not written down anywhere.

For those cases, you need fine-tuning.

Method 2: Fine-Tuning

Fine-tuning permanently modifies a model's weights using your data. The result is a model that "natively" understands your domain without needing context stuffed into every prompt.

When Fine-Tuning Makes Sense

• You need a specific output style or tone consistently
• Your domain has specialized terminology the base model handles poorly
• You want shorter prompts (since knowledge is baked into the model, you do not need to include context every time)
• You have a repetitive task where RAG's retrieval step adds unnecessary latency

Fine-Tuning with OpenAI

OpenAI offers the simplest fine-tuning experience. Prepare a JSONL file with your training examples:

{"messages": [{"role": "system", "content": "You are a legal assistant specializing in contract review."}, {"role": "user", "content": "Review this NDA clause: ..."}, {"role": "assistant", "content": "This clause has three issues: ..."}]}
{"messages": [{"role": "system", "content": "You are a legal assistant specializing in contract review."}, {"role": "user", "content": "Is this non-compete enforceable in California?"}, {"role": "assistant", "content": "Under California Business and Professions Code Section 16600..."}]}

Upload the file and start training through the OpenAI dashboard or API. Fine-tuning GPT-4o-mini costs $3.00 per million training tokens. A dataset of 1,000 examples typically contains around 500K-1M tokens, so the training cost is roughly $1.50-3.00.

Fine-Tuning Open-Source Models

For full control and no per-query fees, fine-tune an open-source model using LoRA (Low-Rank Adaptation). LoRA freezes the base model's weights and trains small adapter layers, reducing GPU memory requirements by 3x or more.

Tools you need:

• Hugging Face Transformers: Model loading and training infrastructure
• PEFT (Parameter-Efficient Fine-Tuning): LoRA implementation
• TRL: Training loop with LoRA support built in
• bitsandbytes: Quantization for QLoRA (4-bit fine-tuning)

A 7B parameter model fine-tuned with QLoRA on 1,000 examples takes about 1-2 hours on a single GPU (A100 or RTX 4090). Total compute cost: $2-5 on cloud GPU platforms like RunPod or Lambda Labs.

For a detailed walkthrough of the fine-tuning process, our guide on how to build a generative AI model covers the full implementation with code examples.

Data Requirements for Fine-Tuning

Quality trumps quantity. Research has consistently shown that a small number of carefully curated examples outperforms a large number of mediocre ones.

Minimum viable dataset:

• 50 examples for simple behavioral changes (output format, tone)
• 200-500 examples for domain adaptation
• 1,000+ examples for complex task specialization

Every example should represent the exact input-output behavior you want. If you want the model to decline questions outside its domain, include examples of it doing so. If you want specific formatting, every output example should follow that format.

Method 3: Custom Training (From Scratch)

Custom training means creating a model from nothing: designing an architecture, assembling a massive dataset, and training for weeks on clusters of GPUs.

When This Makes Sense

Almost never, for most teams. But there are legitimate cases:

• You are building a foundation model as a product
• Your data is in a format or language that existing models handle poorly
• You need a tiny, highly specialized model for edge deployment
• Regulatory requirements prevent you from using models trained on public data

What It Requires

• Data: Hundreds of billions to trillions of tokens for language models. Millions of images for visual models.
• Compute: Training Llama 3 405B required thousands of GPUs running for months. Even a small 1B parameter model needs multiple GPUs for days.
• Expertise: Distributed training, mixed precision, data pipeline engineering, model architecture design.
• Budget: $100K minimum for a small model. $1M+ for anything competitive.

For teams considering this path, frameworks like DeepSpeed, PyTorch FSDP (Fully Sharded Data Parallel), and Megatron-LM handle distributed training. But the engineering challenge goes far beyond the framework choice.

Choosing the Right Method to Train AI on Your Data

Here is a decision framework:

Start with RAG if:

• You have existing documents, FAQs, or knowledge bases
• Your data changes frequently
• You need results fast (days, not weeks)
• You want to use the best available models (GPT-4o, Claude) with your data

Move to fine-tuning if:

• RAG produces inconsistent results
• You need a specific output style the base model cannot match through prompting
• Prompt length and latency are concerns (fine-tuned models need less context)
• You have 500+ high-quality training examples

Consider custom training only if:

• Neither RAG nor fine-tuning meets your requirements after serious attempts
• You have a large ML team and significant compute budget
• Your data is truly unique and cannot be handled by existing models

The Hybrid Approach

Many production systems combine RAG and fine-tuning. Fine-tune a model to understand your domain's language and conventions, then use RAG to give it access to current information. This combination delivers both consistent behavior and up-to-date knowledge.

Privacy and Security When Training AI on Your Own Data

Training AI on your data raises legitimate privacy concerns:

• OpenAI and Anthropic API data: By default, data sent through their APIs is not used for training their models. Verify this in your agreement.
• On-premise options: Run open-source models locally to keep data entirely within your infrastructure.
• PII handling: Strip personally identifiable information from training data. Fine-tuned models can memorize and reproduce training examples.
• Compliance: GDPR, HIPAA, and industry-specific regulations may restrict how you use certain data for AI training. Consult legal counsel.

For more on using AI responsibly in business contexts, see our guides on how to use ChatGPT for work and how to use Perplexity AI for research workflows that keep sensitive data separate.

Getting Started Today

Here is a practical first project:

1. Collect 20-50 of your most common customer questions and their ideal answers
2. Set up a basic RAG pipeline using LangChain and Chroma (local, free)
3. Load your FAQ data and test with real questions
4. Measure answer quality and identify gaps
5. Add more documents (product pages, help articles, internal docs)
6. If quality plateaus, consider fine-tuning for consistent tone and style

For the coding fundamentals, our AI for coding guide covers using AI assistants during the development process.

Make AI Work With Your Data

Generic AI is impressive but limited. AI trained on your data is genuinely useful; it answers questions your customers actually ask, in the language your business actually uses, with information that is actually current.

Start with RAG, graduate to fine-tuning when needed, and save custom training for problems that truly demand it.

FAQ

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