Thoughts on writing PRDs and SRSs with AI assistance
I use AI every day at work to assist me not only in coding but also in handling technical specifications and their challenges. As our organization began adopting AI more broadly, I have seen how the product team has encountered some stumbling blocks in trying to determine how best to use AI to assist them. Even though they are still in the early stages of figuring it out, here are my thoughts on how AI can be used in creating a product requirement document and software requirement specifications.
AI can be useful in project planning, especially when writing a product requirement document (PRD) and software requirement specification (SRS). While some teams might let AI handle the entire process independently, I believe AI works best as a collaborative assistant, helping guide and refine your thinking rather than replacing it entirely.
PRD + AI
Instead of telling it to just write the PRD, there should be a dialog about the product idea. Describe the basic concept and let AI probe deeper.
The product team can collaborate with AI throughout the PRD development process, leveraging its analytical capabilities in these key areas:
- Analyze user research and market data like a requirement analyst and thought partner
- Structure insights and research findings into a coherent product vision
- Identify and suggest additional success metrics that align with business objectives
- Surface implicit assumptions that should be made explicit before development begins
- Ensure the PRD addresses all technical, design, and business dimensions that downstream teams will need
- Synthesize design and product perspectives to identify potential conflicts between business requirements and user experience
- Review the completed PRD for inconsistencies, gaps, or unclear requirements
- Analyze proposed changes and assess their ripple effects across other parts of the PRD
This collaborative approach ensures your PRD becomes a robust foundation that reflects both AI insights and human strategic thinking.
SRS + AI
Once you have a solid PRD developed through this collaborative process, you can leverage that foundation to create an equally robust SRS. Use AI to help write the SRS by giving it the PRD, technical constraints and architecture details. This will help in guiding it to create your vision and architectural philosophy.
When writing the SRS you shouldn't solely rely on AI to write out the parts of the system you may not be familiar with. Development teams can enhance AI-generated specifications by leveraging domain expertise in targeted areas:
- Frontend developers review and refine frontend specifications, ensuring alignment with existing component libraries and user interface standards
- Backend developers review and refine backend specifications, ensuring proper integration with current data architecture and API designs
However, keep in mind that AI may not understand existing legacy requirements or technology limitations in either domain.
AI can be a stress test and help find inconsistencies between the PRD and SRS. It can help identify scenarios where the SRS might not address the user needs in the PRD or how tech constraints might limit the envisioned product UX and may reveal important trade-offs that need to be considered.
Analysis of the PRD and SRS can serve as a technical review before coding that can help spot challenges, ambiguities, and suggest alternative tech approaches. It can also help with determining how changes due to technical constraints or product requirements may affect the system.
AI Output
Quality of the outputted documents (SRS & PRD) depends on specificity and context that is given to the AI. The more you can tell it about the technical environment, user base, architectural constraints, the better the AI assistance will be. It also can help with structural analysis, surface blind spots, and translate between different levels of abstraction.
If you have multiple team members or teams involved in writing the SRS or PRD it would help to develop prompt guidelines and technical vocabulary to ensure that there is a consistent style to the output.
The final product of AI assistance depends on you. Its suggestions may not align with team capabilities or organizational priorities. Ultimately, these are your documents. This collaborative approach—where AI enhances rather than replaces human judgment—ensures you get the benefits of AI's analytical capabilities while maintaining the strategic vision and practical wisdom that only your team possesses.