## Why Fine-Tune Your Own Enterprise LLM?

Commercial LLMs are powerful, but they don't know your company's internal acronyms, product names, or proprietary processes. When an executive asks your AI assistant "What's the status of Project Helix?" — the answer lives in your data, not OpenAI's training set.

Databricks Mosaic AI gives you the infrastructure to fine-tune open-source models (we prefer Llama 3.1 70B for most enterprise use cases) on your private data, with full data governance via Unity Catalog.

## Architecture Overview

``
Your Data (Delta Lake)
→ Unity Catalog (governance + lineage)
→ Mosaic AI Training (fine-tuning)
→ MLflow (experiment tracking)
→ Model Serving (real-time inference)
→ RAG Pipeline (retrieval-augmented generation)
→ Enterprise Applications
`

## Step 1: Prepare Your Training Data in Delta Lake

`python
from pyspark.sql import SparkSession
from pyspark.sql.functions import col

spark = SparkSession.builder.appName("llm-training-prep").getOrCreate()

# Load your enterprise documents
docs_df = spark.read.format("delta").load("dbfs:/enterprise/documents/raw")

# Create instruction-following pairs
training_df = docs_df.select(
col("document_id"),
col("content"),
col("metadata.department").alias("department")
).withColumn(
"training_format",
# Format as instruction-response pairs for fine-tuning
create_training_pair(col("content"))
)

# Register in Unity Catalog for governance
training_df.write.format("delta").saveAsTable("ml.llm_training.enterprise_instructions_v1")
`

## Step 2: Configure Mosaic AI Fine-Tuning

`yaml
# mosaic-ft-config.yaml
model: meta-llama/Meta-Llama-3.1-70B-Instruct
task_type: INSTRUCTION_FINETUNE

training_data:
table: ml.llm_training.enterprise_instructions_v1
split: 0.9

training_params:
learning_rate: 2e-5
num_epochs: 3
batch_size: 8
gradient_accumulation_steps: 4
warmup_ratio: 0.1

evaluation:
metrics: [perplexity, rouge, faithfulness]
eval_table: ml.llm_training.enterprise_instructions_v1_eval

output:
registered_model: main.production.enterprise-llm-v1
experiment: /ml/enterprise-llm-experiments
`

## Step 3: RAG Pipeline for Real-Time Knowledge

Fine-tuning handles style and domain knowledge. For real-time data (ticket statuses, current incidents, live metrics), you need RAG:

`python
from databricks.vector_search.client import VectorSearchClient
from mlflow.deployments import get_deploy_client

# Create vector index on your knowledge base
vsc = VectorSearchClient()
index = vsc.create_delta_sync_index(
endpoint_name="enterprise-vs-endpoint",
source_table_name="main.knowledge.documents",
index_name="main.knowledge.documents_vs_index",
pipeline_type="TRIGGERED",
primary_key="id",
embedding_source_column="content",
embedding_model_endpoint_name="databricks-bge-large-en"
)

# Query pattern
def enterprise_rag_query(question: str) -> str:
# 1. Retrieve relevant context
results = index.similarity_search(
query_text=question,
num_results=5,
filters={"department": user_department} # governance-aware retrieval
)

context = "\n".join([r["content"] for r in results["result"]["data_array"]])

# 2. Augment prompt with context
prompt = f"""You are the Sabalynx Enterprise AI Assistant. Answer based on company knowledge.

Context:
{context}

Question: {question}
Answer:"""

# 3. Call fine-tuned model
client = get_deploy_client("databricks")
response = client.predict(
endpoint="enterprise-llm-v1",
inputs={"messages": [{"role": "user", "content": prompt}]}
)

return response["choices"][0]["message"]["content"]
``

## Production Considerations

1. Data isolation: Use Unity Catalog row-level security so HR data never leaks into engineering responses
2. Hallucination guards: Run every response through a faithfulness check against retrieved context
3. Cost controls: Set token quotas per department via Mosaic AI serving endpoints
4. Model versioning: Every retraining run is a new MLflow model version — never overwrite production

This architecture gives you a model that understands your business, governed by the same data policies you already have in Databricks, and observable through the same MLflow dashboards your data team already uses.