Forget Prompts: Building Declarative AI Pipelines in Fabric
Description
Prompt engineering is powerful but brittle. Hard to scale, harder to maintain. Learn how to build declarative AI pipelines in Microsoft Fabric to tame unstructured data and unlock insights. Discover pitfalls, tools, and techniques to optimize and deliver successful solutions
Key Takeaways
- Prompt engineering is brittle at scale: hard to version, test, and maintain across teams — declarative pipelines solve this by separating what from how
- Declarative approach: you define the desired output schema and constraints; the pipeline figures out how to extract it — much more testable and maintainable than prompt strings
- Most enterprise data lives in free text (tickets, contracts, clinical notes, feedback) — declarative pipelines are the scalable path to structured extraction
- Fabric's AI capabilities (Azure OpenAI integration, notebooks, pipelines) enable end-to-end declarative extraction workflows without leaving the platform
- Key tools: Fabric Notebooks (Python/PySpark + Azure OpenAI SDK), Fabric Data Pipelines (orchestration), OneLake (store extracted structured output)
- Sandeep Pawar is Principal PM at Microsoft on the Fabric CAT team — runs fabric.guru with deep content on Fabric AI patterns
- Pitfalls: LLM output variability, token costs at scale, hallucination in extraction — declarative schemas with validation steps mitigate these
My Notes
Action Items
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