Why You Need a Data Pipeline
When teams hear about RAG (Retrieval-Augmented Generation), they imagine it solves everything. Just embed your documents and plug them into a prompt, right?
But here’s the catch: RAG only works if your data is clean, chunked, structured, and retrievable.
That’s a data engineering problem — not a prompt engineering one.
The Problem: Your Data Isn’t Ready
Most enterprise data is:
- Buried in PDFs, PowerPoints, emails, and portals
- Full of tables, footnotes, and irrelevant noise
- Out of sync with the workflows that rely on it
Your LLM can’t reason over data it can’t parse or doesn’t see.
Without a pipeline, teams end up:
- Hardcoding summaries into prompts
- Copy-pasting examples into context windows
- Duplicating effort across use cases
The Solution: A Real Data Pipeline
A production-grade LLM application needs a data pipeline to:
- Extract content from varied sources (PDFs, HTML, forms, etc.)
- Chunk intelligently — not too small, not too large
- Enrich with metadata (type, owner, validity)
- Embed using model-compatible representations
- Index into a retriever (e.g., Vespa, OpenSearch, Postgres)
- Track versions and sources for traceability
Without these steps, your “RAG system” is just guesswork.
The pipeline is what makes context relevant, reusable, and trustworthy.
LMS Example
Imagine you’re surfacing course policy documents and past grading rubrics in response to student support queries or instructor evaluations.
Without a pipeline:
- The AI fetches outdated, irrelevant, or duplicative context
- It misleads the student, or contradicts your policy
With a pipeline:
- Only the most relevant, tagged, up-to-date sections are retrieved
- You can trace any AI response back to the document and version it came from
That’s how you go from demo to dependable.
The Orcaworks Stack supports full RAG pipelines — from extraction and embedding to indexing and retrieval — with the same observability and modularity built into the rest of the platform.