TL;DR — Direct Answer
As of mid-2026, three LLM applications are genuinely production-ready in data engineering: documentation generation (drafts humans approve), semantic data-quality checks on text fields that regex never handled, and SQL assistance inside guardrails (governed schemas, cost limits, human review for anything that ships). Two are pilot-grade: natural-language analytics on certified semantic layers and incident triage/summarization. Still hype: autonomous pipeline agents writing and deploying production DAGs, schema-mapping magic on messy enterprise sources, and text-to-SQL for unguarded warehouses. The pattern: LLMs excel where outputs are reviewable drafts or bounded classifications — and remain dangerous where outputs are unsupervised writes.
Production-ready today (we ship these)
1. Documentation that stays current
LLMs draft column descriptions, model docs, and PR summaries from SQL + lineage + sample values. Accuracy on first draft: high enough that human review is an edit, not a rewrite. Wire it into CI: undocumented model → generated draft → owner approves in the PR. This converts the eternally-skipped documentation chore into a 30-second review — adoption is the feature.
2. Semantic quality checks on text columns
"Is this address plausibly an address?" "Does this support ticket actually mention the product the row claims?" Classic DQ tooling never handled meaning; classification-shaped LLM calls handle it well, cheaply, in batch. Treat outputs as anomaly signals with confidence scores feeding your observability layer — never as hard gates.
3. SQL assistance inside guardrails
Engineers drafting transformations with an assistant that sees the governed schema and the style guide: real, measured productivity. The guardrails that make it safe: read-only by default, certified-schema scope, cost preview before execution, and human review for anything entering version control. Note what this is: assistance, not autonomy.
Pilot-grade (we deploy with supervision)
4. NL analytics on a semantic layer
Text-to-SQL against a raw warehouse fails on ambiguity ("revenue" means four things) and joins. Against a certified semantic layer with defined metrics, the problem shrinks to intent-mapping — accuracy jumps from coin-flip to dependable for the top hundred question shapes. We build this pattern in production for VipraGo's VipraBot: commands resolve against defined entities and policies, not raw tables — which is exactly why it can be allowed to act.
5. Incident triage and summarization
An LLM reading the failed run, the diff, the lineage, and the last similar incident, then drafting "what broke, blast radius, likely cause": consistently useful, occasionally wrong, always reviewed. Worth deploying; not worth trusting alone yet.
Still hype (we decline to ship)
- Autonomous pipeline agents: "describe your need, the agent builds and deploys the DAG." Demos beautifully on clean schemas; in production the agent confidently ships subtle wrongness — the worst failure class in data, because it lands in finance reports months later. Generated code: yes. Unsupervised deployment: no.
- Magic schema mapping: mapping 400 cryptic ERP columns by name and sample is statistically impressive and operationally insufficient — the errors are precisely on the columns where domain context lives. Use it to accelerate a human mapping exercise, not replace it.
- Open text-to-SQL for business users on raw warehouses: ambiguity + join paths + cost = confident wrong answers delivered fluently. The fix is the semantic layer (above), not a bigger model.
How to evaluate any LLM-in-DE pitch
Three questions cut through every vendor deck: (1) Is the output a reviewable draft or an unsupervised action? Drafts compound productivity; unsupervised actions compound risk. (2) What grounds it? Schemas, semantic layers, lineage, contracts — grounded systems work; vibes-based ones demo. (3) What is the failure mode's blast radius? A wrong column description embarrasses; a wrong MERGE statement restates earnings. Adopt in that order. This is also the build philosophy behind VipraGo: autonomy earned through grounding, approval chains, and audit logs — not autonomy assumed.