Power Platform Innovation Week – Day 6: Prompt Chaining in Power Automate — Orchestrating AI Logic

Power Platform Innovation Week – Day 6: Prompt Chaining in Power Automate — Orchestrating AI Logic

Welcome to Day 6 of Power Platform Innovation Week. Today we're zooming in on a hot architectural pattern for bringing reliable, composable intelligence into business automation: prompt chaining in Power Automate. This article walks you from the why to the how — concrete patterns, a hands-on conceptual walkthrough you can reproduce in your flows, and practical guardrails for production. Expect conceptual clarity, actionable steps, and links to official docs and tutorials so you can follow along and build.

Why prompt chaining matters for automation

Prompt chaining is the practice of connecting multiple LLM prompts (or prompt actions) so that the output of one step becomes the input or conditional driver for the next. Instead of asking a single monolithic question, you break a complex task into discreet, testable stages — extraction, normalization, decisioning, enrichment, and finally presentation — each powered by a tailored prompt. This approach reduces prompt brittleness (small prompt changes don’t break the whole solution), helps you enforce structure, and makes debugging and auditing far easier than a single “kitchen sink” prompt. Conceptually, it’s similar to microservices for AI: components do one thing well and pass standardized results downstream. For an accessible conceptual overview of the pattern, see IBM’s primer on structured prompt chains.

In automation scenarios — invoice processing, contract triage, customer escalation routing, or multi-step content generation — prompt chaining maps naturally to human workflows: read → extract → interpret → act. Microsoft’s Power Platform implements this mindset via prompt actions in AI Builder and the “Run a prompt” experience that can surface variables for later steps in flows. That built-in capability makes it straightforward to build chains without custom code. If you want the official step-by-step on how prompts surface outputs for downstream actions, Microsoft’s documentation covers the “Run a prompt” action behavior and how to reference returned variables.

Beyond single-flow logic, chaining enables hybrid orchestration: you might call a specialized extraction prompt (PDF → JSON), pass the JSON into a validation/normalization prompt (map vendor names to canonical IDs), and then trigger a rules engine or approval flow. This hybrid approach mixes deterministic rules and probabilistic reasoning intentionally — letting the LLM do what it’s best at (parsing messy text, summarizing, synthesizing) while deterministic systems handle authorization and transactional integrity. Microsoft’s posts on generative AI actions and Power Platform explain how prompt actions can be embedded into broader, certified connector workflows and Copilot actions.

Finally, prompt chaining is not just a hack for experimental builds; there’s growing platform support across Azure and Power Platform for visual prompt flows, inputs/outputs, and testing. Azure Prompt Flow and the Power Platform prompts model let you design, test, and re-use modular prompt actions — the primitives you need to scale prompt chains safely. If you plan to move from proof-of-concept to production, consider these platform features early in your design.

How Power Automate supports prompt chaining (core primitives)

Power Automate exposes several primitives you’ll use when chaining prompts: the AI Builder prompt actions (e.g., Create text with GPT using a prompt / Run a prompt), typed inputs (Text, Document, Image, Power Fx), and output variables you can reference in later steps. When you add a prompt action to a cloud flow, the action returns structured outputs (variables) that downstream steps — conditions, Compose actions, connectors — can consume. The Microsoft Learn docs show how to insert prompt outputs into a Teams message, HTTP request, or a subsequent prompt action. This native variable passing is the plumbing that makes chaining straightforward.

Power Automate also supports integrating Azure Prompt Flow or external LLM endpoints when you need more advanced orchestration or model choices. For example, Azure Prompt Flow provides a visual canvas for composing nodes (LLM calls, tools, evaluation steps) and deploying them, while Power Automate can call those endpoints via HTTP connectors or custom connectors. This hybrid model is useful when you need advanced testing and evaluation pipelines or want to reuse the same prompt flow across multiple Power Platform components. The Azure docs and integration guidance show how teams combine the two.

Practical chaining in Power Automate frequently uses a mix of actions: “Run a prompt” for the LLM step; “Parse JSON” or “Compose” to normalize outputs; conditional branches to route based on confidence or extracted labels; and connector actions (SharePoint, Dataverse, Teams, Approvals) to persist results or kick off human workflows. Because prompts can accept typed inputs (like a PDF or a Power Fx expression), you can pass the exact artifact the LLM needs, keep prompts small, and reduce hallucination risk by grounding the model on the source content. Microsoft’s prompts overview and input docs explain the best ways to pass structured content to prompts.

Remember: the platform returns variables and text that are still probabilistic. Power Automate’s deterministic tools (schema validation, conditions, retries) should be used to validate and sanitize every LLM output before trusting it for critical decisions. We’ll revisit the necessary validation steps and patterns in the best practices section.

Design patterns and best practices for chaining prompts

Start modular: design each prompt to solve a single responsibility. One prompt should extract structured fields (like invoice number, date, total), another should map vendor names to canonical records, and a third should draft a human-readable summary for an approval task. Single-responsibility prompts are easy to test, A/B, and swap out for a different model or prompt text without breaking the whole chain. This microtasking approach follows the same engineering benefits as modular code and enables incremental improvements. IBM and Azure writing on prompt flows highlight modular design as a core reliability technique.

Validate outputs aggressively. Use deterministic checks (regular expressions, schema validation via “Parse JSON”, confidence thresholds if the platform provides them) immediately after an LLM step. If a field is missing or confidence is low, route the flow to a retry or human review path rather than proceeding and risking automated actions on bad data. Power Automate’s conditional branching and approval connectors make implementing safe stopgaps straightforward. Many community threads warn about the “injected parameter” and mapping glitches — so plan for defensive parsing and logging.

Control context and token usage. When chaining prompts, include just enough context for the downstream prompt to act — not the entire conversation history unless it’s needed. Large contexts increase cost and risk of leaking sensitive data. Where you must preserve context, extract concise summaries or store canonical intermediate outputs (JSON) and pass the essential fields forward. Azure Prompt Flow and Power Platform both recommend using typed inputs and Power Fx expressions to control exactly what gets sent to the model.

Instrument, test, and iterate. Treat prompt actions as first-class components in your test suite: log inputs/outputs (redact PII), create test data sets, and run A/B comparisons for prompt wording and model choices. Azure Prompt Flow and the Power Platform prompt tooling support testing and evaluation; use them to measure accuracy, latency, and costs. Monitoring also lets you spot drift over time — what the model used to do consistently might degrade as language usage or document templates change.

Hands-on conceptual walkthrough: building a 4-step prompt chain in Power Automate

Scenario: Automate incoming supplier invoices (PDF) to extract fields, normalize vendor, classify urgency, and create an approval request. This walkthrough gives the architecture and the sequence of actions; you can implement it directly in a cloud flow.

Step 1 — Ingest & Extract: Trigger your flow on file upload (SharePoint, OneDrive, email attachment). Add Run a prompt (AI Builder) to extract structured fields from the PDF — invoice number, date, line totals, vendor name. Configure the prompt to return JSON with well-named fields so downstream parsing is deterministic. Use the “Add inputs” guidance to attach the PDF as a document input. Microsoft docs show how document inputs are added to prompt actions and the structured outputs they return.

Step 2 — Normalize & Enrich: Use a Parse JSON action to validate the extractor output. If vendor names are free text, call a second Run a prompt or a lookup against Dataverse to map to canonical vendor IDs. Feed the cleaned fields into this step; keep the prompt focused (“Given vendor text X, return the vendor ID from this list or ‘Unknown’”). If lookup confidence is low, add a branch that flags the record for manual matching. This separation keeps the chain auditable and allows you to swap the mapping method without touching extraction logic.

Step 3 — Classify & Decide: Use a third prompt to classify the invoice (e.g., “is this urgent?” “are there anomalies compared to average vendor invoice amounts?”). Provide the normalized fields and recent vendor spend summary as inputs so the model can compare and make a reasoned judgment. After this prompt, add a condition action: if urgent or contains anomalies, route to expedited approvals; otherwise follow the standard path. Combining LLM judgment with deterministic rules reduces false positives and keeps operational policies enforceable.

Step 4 — Summarize & Act: Use a final prompt to generate a human-readable summary for the approval request that includes extracted fields, anomaly notes, and recommended approver. Post the summary to Teams or create an Approval action in Power Automate with the generated text as the message body. Store the canonical JSON and LLM provenance metadata (prompt version, model identifier, timestamp) in Dataverse or your document store for auditing. This provenance data is crucial for traceability and later model evaluation.

Real-world use cases, risks, and next steps

Common use cases where prompt chaining shines include: document ingestion (invoices, contracts, resumes), multi-step customer support automation (extract → classify → recommend → escalate), content generation pipelines (research → outline → draft → edit), and agentic workflows where a model coordinates specialist tools. Chaining lets you stitch smaller, trustworthy components into complex behaviors that are still maintainable. Industry and platform writeups highlight these scenarios and show how AI + automation unlocks new productivity patterns.

Risk areas to plan for: hallucinations, data leakage, model drift, and over-reliance on probabilistic outputs for transactional decisions. Mitigations include schema validation after every LLM step, redaction of sensitive content before sending to models, human-in-the-loop approvals for high-risk decisions, and continuous monitoring of model accuracy. Platform features (typed inputs, prompt testing, approval connectors) make implementing these mitigations practical — but they must be part of your flow design from day one. Community threads and Microsoft docs both emphasize defensive design and testing.

Power Platform Innovation Week – Day 6: Prompt Chaining in Power Automate — Orchestrating AI Logic

For teams ready to productionize: start with a pilot that uses a narrow, high-value workflow and add telemetry from day one. Use Azure Prompt Flow or Power Platform’s prompt management to version prompts and run A/B tests. When your prompts and chains show stable accuracy, formalize the prompt lifecycle (versioning, review, compliance checks) and integrate governance controls (who can edit prompts, PII scanning, cost alerts). Microsoft’s prompt and Copilot action guidance describe the tooling you’ll need to operationalize prompt chains across the Power Platform.

Wrapping up — what to build next and resources

Prompt chaining in Power Automate turns LLMs from one-off helpers into composable, testable automation building blocks. The pattern encourages modularity, easier troubleshooting, and safer decisioning when combined with deterministic validation and human review. Start small: pick a document type your team processes frequently, implement a 3–4 step chain (extract → normalize → classify → act), instrument it, then iterate. For deeper reading and official how-tos, start with Microsoft’s prompts overview and the “Use your prompt in Power Automate” guide, then explore Azure Prompt Flow for more advanced orchestration and testing.

Source: Power Platform Innovation Week – Day 6: Prompt Chaining in Power Automate — Orchestrating AI Logic