Power Platform Copilot: Low-Code Meets AI

Power Platform Copilot brings AI assistance to Power Apps, Power Automate, and Power BI. Create apps, flows, and reports using natural language. This democratizes development even further.

Power Apps Copilot

Build apps by describing what you want:

class PowerAppsCopilot:
    """Generate Power Apps components from descriptions."""

    async def generate_app_from_description(
        self,
        description: str,
        data_sources: list[str]
    ) -> dict:
        """Generate app structure from description."""

        prompt = f"""Design a Power Apps canvas app.

Description: {description}
Available data sources: {', '.join(data_sources)}

Generate:
1. Screen structure (list each screen and purpose)
2. Data connections needed
3. Key components for each screen
4. Navigation flow
5. Business logic requirements

Format as structured specification."""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )

        return {"app_specification": response.content}

    async def generate_formula(
        self,
        description: str,
        context: dict
    ) -> str:
        """Generate Power Fx formula."""

        context_str = f"""
Controls: {', '.join(context.get('controls', []))}
Data sources: {', '.join(context.get('data_sources', []))}
Current screen: {context.get('current_screen', 'Main')}
"""

        prompt = f"""Generate a Power Fx formula for Power Apps.

Context:
{context_str}

Request: {description}

Provide:
1. The formula
2. Where to use it (e.g., OnSelect, Items property)
3. Explanation"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}],
            temperature=0.1
        )

        return response.content

    async def suggest_improvements(
        self,
        app_structure: dict
    ) -> dict:
        """Suggest app improvements."""

        import json
        structure_str = json.dumps(app_structure, indent=2)

        prompt = f"""Review this Power Apps structure and suggest improvements.

App Structure:
{structure_str}

Analyze:
1. User experience improvements
2. Performance optimizations
3. Accessibility considerations
4. Mobile responsiveness
5. Error handling gaps"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )

        return {"suggestions": response.content}

Power Automate Copilot

Create flows with natural language:

class PowerAutomateCopilot:
    """Generate Power Automate flows from descriptions."""

    async def generate_flow(
        self,
        description: str,
        trigger_type: str = None,
        connectors: list[str] = None
    ) -> dict:
        """Generate flow structure from description."""

        connectors_str = f"Available connectors: {', '.join(connectors)}" if connectors else ""

        prompt = f"""Design a Power Automate flow.

Description: {description}
{f'Trigger type: {trigger_type}' if trigger_type else ''}
{connectors_str}

Generate:
1. Trigger configuration
2. Step-by-step actions
3. Conditions and branches
4. Error handling
5. Variables needed

Format as a detailed flow specification."""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )

        return {"flow_specification": response.content}

    async def explain_flow(
        self,
        flow_definition: dict
    ) -> str:
        """Explain what a flow does."""

        import json
        flow_str = json.dumps(flow_definition, indent=2)

        prompt = f"""Explain this Power Automate flow in plain language.

Flow Definition:
{flow_str}

Provide:
1. Overall purpose
2. Step-by-step explanation
3. When it triggers
4. What data it processes
5. Potential issues to watch"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )

        return response.content

    async def troubleshoot_flow(
        self,
        flow_name: str,
        error_message: str,
        run_history: list[dict] = None
    ) -> dict:
        """Troubleshoot flow errors."""

        history_str = ""
        if run_history:
            import json
            history_str = f"\nRecent runs:\n{json.dumps(run_history[-5:], indent=2)}"

        prompt = f"""Troubleshoot this Power Automate flow error.

Flow: {flow_name}
Error: {error_message}
{history_str}

Provide:
1. What the error means
2. Likely causes
3. Steps to fix
4. How to prevent recurrence"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )

        return {"troubleshooting": response.content}

Power BI Copilot

Create reports with natural language:

class PowerBICopilot:
    """Generate Power BI content from descriptions."""

    async def generate_dax_measure(
        self,
        description: str,
        table_context: dict
    ) -> dict:
        """Generate DAX measure from description."""

        import json
        tables_str = json.dumps(table_context, indent=2)

        prompt = f"""Generate a DAX measure for Power BI.

Data Model:
{tables_str}

Request: {description}

Provide:
1. The DAX formula
2. Measure name suggestion
3. Explanation of the calculation
4. Any related measures that might be useful"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[
                {"role": "system", "content": "You are a DAX expert."},
                {"role": "user", "content": prompt}
            ],
            temperature=0.1
        )

        return {"dax_measure": response.content}

    async def suggest_visualizations(
        self,
        data_description: str,
        business_question: str
    ) -> dict:
        """Suggest Power BI visualizations."""

        prompt = f"""Suggest Power BI visualizations.

Data available: {data_description}
Business question: {business_question}

Recommend:
1. Primary visualization and why
2. Supporting visuals
3. Filters and slicers needed
4. Color scheme suggestions
5. Layout recommendations"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )

        return {"visualization_plan": response.content}

    async def generate_narrative(
        self,
        chart_data: dict,
        context: str
    ) -> str:
        """Generate narrative insights from chart data."""

        import json
        data_str = json.dumps(chart_data, indent=2)

        prompt = f"""Generate a narrative insight for this Power BI visual.

Data:
{data_str}

Context: {context}

Write a 2-3 sentence insight that:
1. Highlights the key finding
2. Provides context
3. Suggests action if appropriate"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )

        return response.content

Copilot Studio

Build custom copilots for specific needs:

class CopilotStudio:
    """Build custom copilots with Copilot Studio."""

    async def design_copilot(
        self,
        purpose: str,
        capabilities: list[str],
        data_sources: list[str]
    ) -> dict:
        """Design a custom copilot."""

        caps_str = "\n".join(f"- {c}" for c in capabilities)
        sources_str = "\n".join(f"- {s}" for s in data_sources)

        prompt = f"""Design a custom copilot.

Purpose: {purpose}

Required capabilities:
{caps_str}

Available data sources:
{sources_str}

Design:
1. Conversation topics
2. Trigger phrases for each capability
3. Data integrations needed
4. Escalation scenarios
5. Persona and tone guidelines"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )

        return {"copilot_design": response.content}

    async def generate_topic(
        self,
        topic_name: str,
        user_intent: str,
        required_info: list[str]
    ) -> dict:
        """Generate conversation topic."""

        info_str = "\n".join(f"- {i}" for i in required_info)

        prompt = f"""Create a conversation topic for a copilot.

Topic: {topic_name}
User intent: {user_intent}
Information to collect:
{info_str}

Generate:
1. Trigger phrases (5-10)
2. Conversation flow
3. Clarifying questions
4. Confirmation messages
5. Error handling responses"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )

        return {"topic_definition": response.content}

Best Practices

1. Start simple - Begin with straightforward automation
2. Validate output - Always review AI-generated formulas
3. Test thoroughly - AI suggestions may need refinement
4. Document - Record what Copilot helped create
5. Iterate - Refine prompts for better results

Power Platform Copilot makes citizen development more accessible than ever. Combined with proper governance, it enables rapid digital transformation.