Having Smarter Conversations with AI
Go beyond single-turn prompts and learn how to collaborate with AI like a product teammate — not just a tool.
This post is part of the Talking to AI series, which features Unlocking Generative AI's Potential for Faster, Better Product Management every week.
So far, you’ve learned foundational techniques for structuring prompts, refining outputs iteratively, and integrating basic external data. These methods have been geared primarily toward single-turn prompts — where you ask one question or give one instruction and receive one response.
But product management rarely works that way.
Real product challenges often demand:
Deeper reasoning
Layered context
Multiple perspectives
This is where multi-turn dialogues and advanced prompting unlock the next level — transforming AI from a transactional tool into a flexible thinking partner.
In this chapter, we’ll explore how to:
Guide AI through extended interactions
Role-play different stakeholder perspectives
Handle specialized tasks like code generation or content creation
Combine prompting frameworks for large or messy product scenarios
Moving Beyond Single-Turn Prompts: Multi-Turn Dialogues and Visual Context
A single-turn prompt is useful when your question or request is straightforward (e.g., “List three benefits of feature X”). Yet product decisions often evolve through iterative discussions, each step informed by new data or constraints. With multi-turn dialogue, you can supply additional context or ask follow-up questions in real-time, letting the AI refine its output and mirror a more natural, conversation-like dynamic.
The goal isn’t just a better output. It’s practicing how to think with AI — step by step, constraint by constraint, just like excellent product work.
Example:
Initial Prompt: Suggest three improvements to our onboarding flow that might reduce user confusion.
LLM Response: Propose a simplified signup screen, an interactive tutorial, and a guided walkthrough.
Follow-up Prompt: Of these three, which is most feasible to implement in under two sprints, given our small mobile dev team? Provide a brief risk analysis.
Over multiple turns, you zero in on practical, actionable answers. This structure prevents one “dump” of information and instead surfaces deeper insights at each stage.
Incorporating visual elements into your workflow can improve engagement and clarity when refining AI-generated outputs. Instead of relying solely on textual exchanges, images, videos, and structured datasets can provide richer context and support decision-making. This approach enables:
Dynamic Feedback Loops: Upload user flow diagrams or screenshots to refine onboarding improvements collaboratively.
Visual and Textual Synthesis: Combine text inputs with charts, wireframes, or annotated documents for richer discussions.
Cross-Disciplinary Collaboration: Address engineering, design, and marketing perspectives in a single dialogue by sharing multimodal inputs.
Example:
Initial Prompt: Here’s a screenshot of our current landing page. Based on this image and the attached survey feedback, suggest three ways to improve conversions.
Follow-up: Which of these suggestions requires minimal redesign effort? Rank them by feasibility.
Role-Playing Scenarios and Perspective Shifts
Another advanced strategy is to assign the AI a specific persona or viewpoint. This goes beyond simply “speaking to a CFO” for budget concerns or “addressing marketing leads.” By role-playing, you instruct the AI to embody different stakeholders' or user types' knowledge and priorities. This can reveal blind spots or concerns you might otherwise miss.
Example:
Context: You suspect your next feature might face security scrutiny.
Prompt: Adopt the role of our enterprise security consultant, who is extremely cautious about data compliance. Provide three specific concerns about implementing single sign-on via social media accounts and one solution for each concern.
Here, the AI’s response should reflect that security mindset. It should highlight issues around token handling, third-party data sharing, and identity management and help you proactively address them before proceeding further in the product lifecycle.
Handling Specialized Tasks
Beyond user stories or general product discussions, LLMs can be directed to perform particular functions. While these capabilities are powerful, they also introduce specific challenges that require careful oversight.
Beyond user stories or general product discussions, LLMs can be directed to perform particular functions. While these capabilities are powerful, they also introduce specific challenges that require careful oversight. However, these tasks come with limitations that require careful consideration. For instance:
AI-generated code may include inefficiencies, security vulnerabilities, or compatibility issues, necessitating thorough human review and testing.
In data analysis, biases in training data can lead to misleading insights, and human oversight is critical to validate findings and ensure they align with business objectives.
Additionally, AI-generated content may not always conform to brand voice, legal standards, or compliance regulations, making manual review essential before publication. Understanding these constraints helps ensure AI is applied effectively without introducing risks or inefficiencies.
As a PM, deciding when to leverage AI versus when to rely on human judgment is crucial. AI excels at automating repetitive tasks, summarizing large datasets, and generating structured outputs, but human oversight is necessary for complex decision-making, ethical considerations, and nuanced problem-solving. A helpful approach is implementing a validation layer where AI-generated suggestions undergo a human review before implementation.
Managing Context and “Memory”
Some LLM platforms allow conversations to retain context, but their capabilities vary. Understanding whether an AI system relies solely on token context or offers memory across sessions.
Some LLM platforms allow your conversation to retain context, meaning if you mention a user persona or a file in an earlier step, the AI can refer back to it later. However, different platforms handle memory in varying ways. For example, OpenAI's ChatGPT has a memory feature that allows it to retain information across sessions, enabling more personalized interactions. Users can manage, update, or reset this memory to refine the AI’s contextual awareness. However, when memory is disabled, ChatGPT relies solely on token context, meaning it can only remember information within the active session and forgets previous interactions once the session ends. Understanding how ChatGPT's memory and token context differ is crucial for structuring prompts effectively. Understanding these differences can help optimize prompt strategies based on the platform's capabilities and limitations. However, every platform has limits. You may need to summarize or restate relevant points if the discussion grows lengthy.
Best Practices
To maximize the effectiveness of multi-turn dialogues, consider structuring AI interactions using a combination of clear summarization, segmentation, and drift management techniques.
Brief recaps: Provide any historical context or information about prior conversations that can help generate an effective response from the AI. For example:
Prompt: Here’s a quick summary of our conversation so far: we targeted mid-market clients, discovered a 20% drop in usage, and suspect complex onboarding is partially to blame.
Segment lengthy tasks into smaller steps: Instead of one massive prompt referencing multiple data sources, break it down into phases.
Use summarization techniques: When dealing with extended conversations, periodically summarize key insights and decisions to ensure continuity and prevent the loss of essential details.
Handle AI drift proactively: If responses deviate from the intended topic, steer the AI back by reinforcing key context or constraints.
Optimize memory constraints: Be mindful of token limits in LLMs. If memory is not retained across sessions, reintroduce the necessary context explicitly at the start of new interactions.
Product-Centric Pitfalls and Considerations
Lengthy conversations can become limited due to context constraints, as most LLMs have token limits that restrict how much prior conversation they can retain. As a best practice, when discussions grow too long, and context starts degrading, consider starting a new conversation to reset the AI’s understanding and improve response accuracy.
While multi-turn dialogues and role-playing can produce richer AI output, they also add complexity:
Overcomplicating persona switches may confuse the model if changes are abrupt or contradictory.
Fact-checking remains vital when the AI produces advanced content or analysis since it relies on patterns rather than verified data. Additionally, AI-generated insights may reinforce biases present in training data.
PMs should validate AI-driven recommendations with real-world user data before making critical product decisions.
If AI provides an overconfident response that seems misaligned with actual user behavior, further scrutiny is necessary to avoid misleading conclusions.
Balancing multiple frameworks is powerful, but avoid excessive instructions that might conflict or overwhelm the AI.
Proceed methodically, introducing new contexts or personas step by step. If an answer veers off-track, reframe the prompt with updated constraints or a reminder of the context so far.
Bringing It All Together
Advanced prompting opens up dynamic, iterative exchanges with AI. By incorporating multimodal inputs, refining concepts step by step, and combining frameworks strategically, you can guide LLMs to produce highly relevant, actionable insights. With careful context management and structured guidance, these tools become flexible, context-aware collaborators that adapt to your changing needs.
Reflection Exercise
Try this with your next product challenge:
Pick a real product problem you're working on - ideally, one that feels messy, layered, or still a bit unclear.
Then run this 4-step multi-turn prompt sequence with AI:
1. Start Simple
Ask a single-turn question to get initial ideas.
Example: "Suggest three ways to improve our onboarding experience."
2. Go Deeper
Add real-world constraints or context from your team.
Example: "Which of these ideas could our small mobile dev team implement in under two sprints? Provide pros and cons."
3. Shift Perspective
Ask AI to role-play a critical stakeholder or user.
Example: "Now act like our most skeptical enterprise customer. What concerns might they have about these ideas?"
4. Summarize + Recommend
Ask for a concise recommendation based on everything so far.
Example: "Summarize the best idea for our next release — including why it works, risks to watch, and next steps."
Bonus Challenge: Try Visual Inputs
Upload a screenshot, diagram, or survey result to see how it changes the AI’s response.
Reflect:
What changed between your first and final prompt?
What new perspective emerged from role-playing?
Where did AI struggle?
Where did it surprise you?