The “Training” Misconception

In 2025, every CTO has the same mandate: “We need our own AI.” They come to us and say: “We want to train a model on our data so it knows our product catalog.” When they say “Train”, they imagine Matrix-style learning. Upload the data, and the AI knows kung fu. This is a fundamental misunderstanding of how Large Language Models (LLMs) work. They assume the pipeline is: Documents -> Fine-Tuning -> Smart Model. In reality, Fine-Tuning is almost never the right tool for knowledge injection. To understand why, we need to distinguish between Procedural Memory (Skills) and Semantic Memory (Facts). Fine-Tuning teaches the model how to talk. RAG teaches the model what to talk about.

Why Maison Code Discusses This

At Maison Code, we build Enterprise AI systems. We see companies burning $50,000 to fine-tune Llama 3 on their documentation, only to find out that the model still hallucinates. “Why did it say the product costs $50? We updated the price to $60 in the dataset!” Because Weights are Sticky. Once a model learns a fact during training, it is hard to un-learn it. We implement RAG (Retrieval Augmented Generation) architectures because they are dynamic, cheaper, and grounded in truth. We save our clients from the “Training Trap”.

The Student Analogy

Imagine you are sending a student (the LLM) to a Biology exam (The User Query). The student is smart but doesn’t know the specific curriculum of your university (Your Business Data).

Approach 1: Pre-Training (The Child)

This is building the brain from scratch. You teach the child to read, write, do logic, and understand the world. Cost: $100 Million + 10,000 GPUs. Who does this: OpenAI, Google, Meta, Mistral. You should NEVER do this. Unless you are a Sovereign Nation.

Approach 2: Fine-Tuning (The Med School)

You take a smart student and send them to medical school for 4 years. They behave like a doctor. They speak like a doctor using Latin words. They write prescriptions correctly. But do they know the blood pressure of Patient John Doe right now? No. Because they graduated yesterday. They don’t have access to the live patient file. Fine-Tuning changes Behavior and Style. It teaches the model new syntax (e.g., “MaisonScript”), or how to be rude/polite, or how to output JSON. It is NOT good for facts, because facts change. Cost: $1,000 - $10,000.

Approach 3: RAG (The Open Book Exam)

You take a smart student. You don’t send them to med school. Instead, you give them a massive textbook (Your Database) and say: “You can look up the answer during the exam.” When the question comes (“What is John Doe’s blood pressure?”), the student searches the book, finds the page, reads it, and generates the answer. RAG (Retrieval Augmented Generation) handles Knowledge. Cost: $0.01 per query.

Deep Dive: Retrieval Augmented Generation (RAG)

RAG is the architecture of choice for 95% of Enterprise AI applications. It solves two massive problems:

Hallucination: The model is forced to use the provided context. If the context says “Sales were $5M”, the model won’t guess “$10M”.
Staleness: You don’t need to re-train the model when your inventory changes. You just update the database.

The RAG Stack

Ingestion:
- Take your PDFs, Notion docs, SQL Database.
- Chunking: Split them into small pieces (e.g., 500 words). Overlap them by 50 words to preserve context.
Embedding:
- Pass each chunk through an Embedding Model (OpenAI text-embedding-3-small or Cohere).
- This converts text to a Vector (a list of 1536 numbers).
Vector Database:
- Store these vectors in Pinecone, Weaviate, or pgvector.
Retrieval:
- User asks: “Do we have red shirts?”
- Convert question to vector.
- Search Database for “Nearest Neighbors” (Cosine Similarity).
- DB returns: “Red Shirt Bundle - Stock: 50”.

Generation:

Construct Prompt:

You are a helpful assistant. Answer the user question based ONLY on the context below.
Context: "Red Shirt Bundle - Stock: 50"
Question: "Do we have red shirts?"
Answer:

LLM answers: “Yes, we have 50 in stock.”

When needed: Fine-Tuning (Domain Adaptation)

So is Fine-Tuning useless? No. It has specific use cases where RAG fails.

Use Case 1: The Code Generator You have an internal programming language called “MaisonScript”. GPT-4 has never seen it. RAG won’t help because if you retrieve a snippet of code, the model still doesn’t understand the grammar or compiler rules. You Fine-Tune Llama 3 on 50,000 lines of MaisonScript. Now it “speaks” the language fluently.

Use Case 2: The Brand Voice You are a luxury brand. You never use emojis. You always sound slightly aloof and French. GPT-4 default personality is “Cheerleader functionality”. Prompt Engineering (“Don’t use emojis”) is weak. It forgets. You Fine-Tune it on 1,000 past emails from your concierge team. Now it adopts that persona naturally 100% of the time.

Use Case 3: Latency & Cost Reduction GPT-4 is expensive and slow. You can use GPT-4 to generate training data (Questions + Perfect Answers). Then you Fine-Tune a tiny model (Mistral 7B or GPT-3.5) on that data. The tiny model learns to mimic the big model. Now you run the tiny model for 1/10th of the cost and 10x the speed. This is Distillation.

The Cost/Benefit Analysis

Feature	RAG	Fine-Tuning
Knowledge Source	Dynamic (Real-time DB)	Static (Training Set)
Setup Time	Days	Weeks/Months
Maintenance	Low (Auto-sync)	High (Re-train on every drift)
Accuracy	High (Grounded)	Medium (Hallucinations possible)
Cost	Storage + Embeddings	Compute (GPU Training)
Best For	QA, Search, Analysis	Style, Tone, Code, Logic

11. Evaluation: How do you know it works?

“The model looks good.” -> This is not engineering. We use RAGAS (Retrieval Augmented Generation Assessment) framework. It measures:

Faithfulness: Does the answer rely only on the context?
Answer Relevance: Did it actually calculate the blood pressure?
Context Precision: Did the database return the right page? We run this evaluation suite in CI/CD. If the model accuracy drops below 90%, the deployment fails.

12. Vector Database Scaling (The 10M Limit)

Pinecone is great for 100k vectors. What about 100 Million? At scale, “Exact KNN” (finding the perfect match) is too slow. We use HNSW (Hierarchical Navigable Small World) index. It’s an approximate search (ANN). It trades 1% accuracy for 1000x speed. We also enable Hybrid Search (Keyword + Vector) to handle exact SKU searches (“Show me SKU-123”) which vector search is notoriously bad at.

13. Data Curation: Garbage In, Garbage Out

Training on 100 bad examples is worse than training on 0. If you train a model on your Customer Support logs, and your agents are rude, the AI will be rude. Data Curation is 80% of the work.

Deduplication: Remove identical questions.
PII Stripping: Remove emails and phone numbers.
Gold Standard: Have a Senior Human rewrite the answers to be perfect. We built an internal tool “Maison Annotate” to help teams clean their datasets before a single GPU is spun up.

14. Efficient Training: LoRA (Low-Rank Adaptation)

Full Fine-Tuning updates 70 Billion parameters. This requires 8 x H100 GPUs ($30/hr). LoRA freezes the main weights and only trains a tiny “Adapter” layer (1% of parameters). Result: You can train Llama 3 on a single consumer GPU (RTX 4090). The adapter file is only 100MB. You can hot-swap adapters at runtime:

User A talks to “Medical Adapter”.
User B talks to “Legal Adapter”. All served from the same base model.

15. Conclusion: The Hybrid Future

The best systems use both. We call this Fine-Tuned RAG.

Fine-Tune a small, efficient model to be really good at reading your specific document format and outputting your specific JSON schema.
Use RAG to feed that model the latest facts from the database. This gives you the reliability of a specialist (Fine-Tuning) with the knowledge of an encyclopedia (RAG). Don’t choose. Combine.

Model hallucinating?

If your AI chatbot is lying to your customers, or your “Training” project failed to deliver results, Maison Code can re-architect your pipeline. We implement production-grade RAG systems using Pinecone and LangChain to ground your AI in truth.

Hire our Architects.