The full path to a pipeline that moves data without corrupting it — design the ingestion and transforms, extract and structure the sources, gate the quality, store it, then deliver and ship it monitored.
A data pipeline is judged on one thing: does data come out the other end correct, or quietly corrupted? Unlike a retrieval system, it isn't there to answer questions, and unlike an API backend, it isn't there to serve requests — it exists to move data through stages, transforming and checking it on the way, and the failures are silent: a dropped row, a doubled record, a malformed value that becomes a wrong number three dashboards later. This project builds the pipeline with that danger front of mind. It designs the ingestion and the transformation stages, extracts and structures the raw sources into a clean shape, gates the data with validation before anything is stored, designs the schema it lands in, exposes the processed data through a delivery API, and ships it with the monitoring that catches a silent failure before it spreads. It owns the ingestion → transformation → validation → storage → delivery journey specifically — not the retrieval of a RAG system, not the request/response of an API backend. Each stage connects to a NewPrompt workflow you can run on its own; together they carry data from raw sources to a trustworthy, delivered output. You own the data and the infra; the project makes the pipeline correct by design, not by hope.
Each stage runs a NewPrompt workflow, with a supporting resource and tool. Work them in order — the output of each stage feeds the next.
See the execution map →Design the ingestion and transforms
Map where the data comes from and how it moves — the sources, the ingestion, and the transformation stages from raw input to target shape — as a designed pipeline, not one opaque script.
Design the storage schema
Model where the processed data lands — the tables, relationships, and indexes the pipeline writes to and downstream consumers read from — so storage fits the data instead of forcing it.
Extract and structure the sources
Turn the raw source data — documents, exports, API payloads — into the structured shape the pipeline works on, so every downstream stage runs on clean, predictable input.
Gate the data with validation
Decide what a valid record looks like and check it at a gate — schema conformance, ranges, required fields, duplicates — so bad data is caught and quarantined before it reaches storage and becomes a wrong number downstream.
Deliver the data via an API
Expose the processed data to its consumers through a delivery API — the contract downstream services and dashboards read from — so the pipeline's output is usable, not just stored.
Ship and monitor the pipeline
Deploy the pipeline with the monitoring a silent-failure system demands — signals on volume, freshness, and quality — because a pipeline you can't observe is one you'll only fix after the bad data has spread.
A data pipeline you can trust — ingestion and transforms designed, sources extracted into a clean shape, validation gates stopping bad data before storage, a target schema, a delivery API, and the whole thing shipped with monitoring — data that arrives correct, with a way to know the moment it doesn't.
A validation gate. The journey designs the pipeline to check data at the boundary so a bad source row does not silently poison everything downstream.
Both — AI helps design each stage, and AI steps like extraction and classification can live inside the pipeline. Those steps get evaluated like any other.
Delivery — the data leaves the pipeline through an API, and a monitoring stage keeps it healthy after ship.
You need your raw source shapes and a rough definition of a valid record before you start. Stage one designs ingestion and transforms around real sources — documents, exports, or API payloads — and stage four models the target schema, but you supply those source shapes and the fields downstream consumers actually read.
Yes. The stage-one design workflow maps either batch or streaming ingestion into the same ingestion-to-delivery pipeline, so both are in scope. The validation gate, storage schema, and monitoring on volume and freshness apply to either mode; you decide the trigger cadence for your own runtime.
You validate that the gate quarantines bad rows, extraction stays correct on real sources, and the volume-and-freshness monitors fire before production. NewPrompt gives you the stage design and prompts across ingestion, validation, and monitoring, but you run the pipeline on your own infra and prove it moves data without corrupting it.
Design a pipeline that moves data without corrupting it — map the sources and ingestion, design the transformation stages, set validation and quality gates, then document the pipeline and monitoring.
Turn messy text into structured data you can trust enough to feed another system — bound the source, extract the fields, force clean JSON, and validate before it flows downstream.
Make any AI task return JSON your code can rely on — define the schema, force the model to it, validate every response, and diff the drift when a model update breaks the shape.
Design a schema on its data, not a hunch — model the entities and relationships, set the constraints that protect integrity, plan indexes around real queries, then document the schema and migration.
Design an API on its contract instead of discovering it endpoint by endpoint — model the resources, design the endpoints and payloads, pin the contract, then review it before code locks it in.
Cross the gap between 'tests pass' and 'safe in production' — assess release readiness, plan the deploy and its rollback, and set up the monitoring and launch checks before you ship, not after.
A data analyst role prompt with statistical honesty built in — clarify the decision first, treat correlation as a hypothesis, and never launder uncertainty into precision.
Free text in, named fields out. The extraction prompt pattern that turns any unstructured text into consistent, parseable records.
Fields checked against the contract: missing ones flagged, invented ones caught, prose around the object detected.
Map every predicate, join, and sort to the index that serves it — or doesn't. Composite order rules, covering decisions, and the write tax nobody mentions.
Architecture review for the public surface: abstractions that earn their place, dependency direction, seams, and the patterns the codebase already has.
SHIP or DO NOT SHIP: twelve checks on failure modes, observability, rollback, and load — the review that happens before the incident.
Turn product requirements and key entities into an ERD-ready schema spec: tables, fields, primary and foreign keys, relationships with cardinality, indexes, and constraints — with the modeling gaps named as open questions.
You have the schema — fields, types, requirements. The translation into a prompt the model actually follows: schema lines, realistic example, and validation rules.
Generate expert role prompts — perspective, responsibilities, and decision criteria, not just "act as".
Build prompts that extract defined fields from unstructured text — emails, invoices, tickets, résumés.
Paste an AI's output and validate it against the expected format — with a repair prompt for every failure found.
Build evidence-based SQL optimization prompts — goal, platform, and the evidence you have turn into a query tuning contract.
Build prompts that return structured data — JSON first, with YAML, XML, and CSV modes — parseable every time.
Build prompts that produce documents in a fixed structure — headings, sections, and tables.
AI models return broken JSON more often than you'd expect. Here's how to structure a prompt so the output parses cleanly, plus what to check before you trust it.
Practical project ideas you can build from this base project path — each opens in the Project Advisor.
The full path to a retrieval system that returns grounded answers — understand the corpus, chunk and ground it, extract and classify the metadata, then evaluate that retrieval actually works.
The full path to automation that survives the real world — wire the integrations and triggers, design the control API, move the data through validated stages, evaluate the AI steps, then deploy.
The full path to knowledge that's findable by people and AI — plan the taxonomy, structure it for search, write the articles, tag the metadata, make it retrievable, then ship it maintainable.
The full path to a backend you can put clients on — define the requirements, design the architecture, API contract, data model, and access control, then build it reviewed, tested, secured, and shipped.