Most generative AI programs stall for data reasons long before model choice becomes the constraint. The demo ran on a few hundred hand-picked documents. Production has duplicates, retired policies, missing permissions and nightly jobs that fail without anyone noticing.
An LLM turns a stale chunk into a confident, fluent answer. That is why "the model is hallucinating" should be treated as a data pipeline bug until retrieval metrics prove otherwise.
Prompt tuning without measurement is guesswork. A frozen golden set and three layers of checks turn every pipeline change into a release you can gate.
Track freshness SLAs per source, duplicate and near-duplicate rates, parse failures, empty chunks and documents missing ACL metadata. Run checks with dbt tests, Great Expectations or Soda, and use observability tools to alert on volume and schema drift.
Build a golden set of a few hundred real questions with known correct sources, drafted by subject matter experts. Score the retriever with recall@k and mean reciprocal rank before touching the prompt.
Score faithfulness, correctness and citation accuracy with frameworks such as Ragas, TruLens, DeepEval or MLflow. Calibrate LLM-as-judge scores against human review, then gate every release on all three levels.
Cover ingestion, parsing and chunking, embeddings, vector search, lakehouse tables, metadata and lineage, with owners and tests for each.
Choose between ETL, ELT and streaming based on PII control, freshness needs and the cost of re-embedding.
Use a frozen golden set, incremental re-embedding, storage tiering and FinOps tagging to keep quality up and spend down.
Follow a three-phase plan and an eight-dimension maturity scorecard, then reuse the pattern for the next use case.
Building the pipelines, storage, metadata and governance that feed LLMs trusted context. It extends classic ETL and warehousing with document parsing, chunking, embeddings, vector search, permission enforcement, lineage and evaluation.
Pilots use small, curated document sets, while production exposes duplicates, stale versions, missing permissions and parsing errors. Gartner predicted at least 30% of GenAI projects would be abandoned after proof of concept by the end of 2025, citing poor data quality first.
Default to ELT so you can re-chunk and re-embed when models change. Use ETL when sensitive fields must be masked before storage, as with PHI. Add streaming only where answers must reflect changes within minutes, such as live support or fraud workflows.
Iceberg adds transactions, schema evolution and time travel to files in object storage, and lets Spark, Snowflake, BigQuery, Athena and Trino share one copy of data. Iceberg v3 row lineage identifies changed rows, so pipelines re-embed only what changed.
Embed incrementally using CDC, tier vector storage between object storage and low-latency engines, compact lakehouse tables, use batch inference for enrichment and tag spend by product. The biggest avoidable cost is a full re-embed.
A data platform engineer, one or two data engineers for unstructured pipelines, an AI or ML engineer for retrieval and evaluation, an analytics engineer and a part-time governance lead. A business product owner should sign off on the golden evaluation set.
CTOs, VPs of Engineering and data leaders moving GenAI from pilot to production, and the platform engineers who will own the pipelines.
Drop your details and we'll send Data Engineering Foundations for Generative AI straight to your inbox - no spam, unsubscribe anytime.
Talk through your GenAI data foundation with our data and AI engineering leads. A working session, not a sales pitch. SECTION 7 - FAQ - 5 to 8 questions
Talk to our engineers