Leveraging AI for Product Research and Documentation
The real power of AI in product development lies in connecting external research with internal knowledge to create valuable insights and documentation
In today's fast-paced product development environment, AI tools have become invaluable for streamlining research and documentation processes. Let me walk you through a practical workflow using various AI tools to conduct research, analyze competitors, and create product documentation.
AI-Powered Research: The Perplexity Advantage
When starting a new project—such as developing a customer insights dashboard—the first step is gathering industry best practices and understanding the competitive landscape. While ChatGPT provides insights based on its training data, Perplexity AI offers a distinct advantage for real-time research.
Why Use Perplexity?
Real-time search: Perplexity pulls from multiple sources across the internet, ensuring up-to-date insights.
Comprehensive responses: It provides information with citations from various sources, offering a well-rounded perspective.
Organized research: Insights can be saved to project-specific spaces for easy access.
For example, when researching best insights for a customer research dashboard, Perplexity returned valuable information on heat maps, session recordings, user journey analytics, and time-on-task measurements, all with sources for verification.
Choosing the Right AI Model
Different AI models excel at different tasks:
Sonnet models (Claude): Best for conversational and creative writing tasks.
GPT-4o: A strong general-purpose model.
Perplexity AI models: Optimized for search and research-based tasks.
Advanced models (GPT-4o-mini, Claude Opus): Offer deeper reasoning but may have usage limits.
For research, I’ve found that Claude Sonnet 3.5 often outperforms GPT-4, but the best model depends on your specific needs.
Competitive Analysis Made Simple
Once initial insights are gathered, AI can be used to analyze the competitive landscape. For instance, asking AI about common complaints in user insight dashboards or identifying top tools (e.g., Amplitude, Mixpanel, Hotjar) yields valuable competitive intelligence.
Using AI for SWOT Analysis
By requesting a SWOT analysis, AI can generate strategic insights that traditionally required hours of manual research. A sample analysis might reveal:
Amplitude: Strong brand recognition but relatively high pricing.
Mixpanel: User-friendly interface but limited data visualization capabilities.
Industry Opportunities: AI-driven insights and automation.
Competitive Threats: Emerging analytics platforms with lower costs.
This structured approach helps teams make data-driven strategic decisions quickly.
From Research to Documentation
Once research is complete, the next step is transforming insights into actionable documentation. AI-powered tools like ChatGPT’s Canvas and Claude’s document editor assist in creating PRDs (Product Requirements Documents) and strategy reports.
Addressing the Context Challenge
General AI-generated documents often lack context about your specific product. To improve quality, consider:
Providing detailed manual context: Describe your product, company, and objectives explicitly.
Integrating research findings: Export external research as input for document generation.
Using specialized tools: Platforms like Build Better store internal knowledge and maintain organizational context.
The key to high-quality documentation is ensuring AI understands your product’s nuances, customer needs, and strategic priorities.
Bringing It All Together: A Practical Workflow
The most effective AI-powered workflow includes:
Using Perplexity AI for external research: Gather competitive insights and industry trends.
Organizing findings by project: Maintain structured research repositories.
Providing context to AI: Ensure generated content aligns with your product strategy.
Generating documentation with AI: Draft PRDs, competitive analyses, and strategy documents.
Refining AI output: Edit and align AI-generated content with your business goals.
This approach transforms AI from a generic content generator into a powerful assistant that accelerates product development while maintaining strategic relevance.
Final Thoughts
The real power of AI in product development lies in connecting external research with internal knowledge to create valuable insights and documentation. Whether analyzing competitors, drafting product requirements, or synthesizing user research, the right combination of AI tools can enhance efficiency and decision-making.
What research challenges are you facing in your product development process? The right AI approach might be the key to unlocking new efficiencies and insights.
