· LLM Finetuning · 2 min read
Preparing Your Enterprise Data for LLM Training
Garbage in, garbage out. The success of your custom LLM depends entirely on the quality of your training dataset. Here is the blueprint.
In the world of Generative AI, code is cheap; Data is Gold. Training an AI on messy, unorganised corporate documents will produce a model that is confident but confused. The secret to a world-class model lies in the data preparation.
The Data Pipeline
1. Extracting the Knowledge
Most company knowledge is locked away in PDFs, PowerPoint slides, and emails.
- The Problem: Normal tools destroy the layout (headers, tables) when reading these files.
- The Solution: We use smart vision-based tools to read documents just like a human does, preserving the structure.
2. Cleaning & Privacy
Before training, we must scrub the data clean.
- Privacy: We use tools to automatically find and replace names, emails, and credit card numbers with placeholders, so the AI doesn’t learn sensitive info.
- De-duplication: If the AI reads the same sentence 100 times, it will memorise it perfectly but struggle to learn anything else. We remove these duplicates.
3. Teaching the Format
We transform raw text into Q&A pairs to teach the model how to be an assistant:
- Input: “User: What is the refund policy?”
- Output: “Assistant: According to the 2024 policy, refunds are processed within 14 days…”
Synthetic Data
Sometimes, you just don’t have enough data. In those cases, we use a smarter, larger model to read your documents and generate practice questions for the smaller model to learn from. This works surprisingly well.
Alps Agility brings decades of Data Engineering experience to AI. We don’t just tune models; we build the pipelines that feed them.
Is your data AI-ready? We offer a Data Readiness Assessment to help you prepare. Start the conversation.
