Data Foundations for LLMOps
Explore how to transform raw, unstructured documents into clean, semantically complete chunks for LLMOps ingestion. Understand data cleaning, structure-aware splitting, and system guarantees ensuring safe, repeatable, and reliable retrieval-ready data preparation.
A common misconception about LLM systems is that intelligence resides entirely within the model.
Engineers often assume that choosing a more advanced model automatically yields better results. In a RAG system, this assumption quickly breaks down. A RAG system is only as good as the context it retrieves. If the retrieval layer feeds the model broken sentences, noisy markup, or irrelevant text, the model will fail regardless of its power.
This is the principle of garbage in, garbage out.
In this lesson, we will build the ingestion pipeline. We will learn how to transform raw, messy text files into clean, semantically complete units of information. We will accomplish this using Python and LangChain, a popular combination widely used in industry.
Before we touch code, we need to understand why ingestion is not a trivial preprocessing step in LLM systems. Many production failures attributed to bad models are actually caused by poor document preparation.
Unstructured data engineering
To see why this matters, consider the following scenario. A user asks an HR assistant:
What is the policy on maternity leave?
The bot replies:
According to the document ‘Page 4 Confidential Draft Not for Distribution Maternity leave,’ the period is 12 weeks.
This behavior is deterministic, not a model hallucination.
The model is reproducing text present in the input. A PDF parser merged a page footer into the main text due to a lack of layout awareness. This illustrates a practical difference between traditional MLOps and LLMOps. In traditional MLOps, data engineering focuses on structured inputs such as CSV files or SQL tables. ...