Building an effective chatbot or voice assistant requires properly setting and managing expectations. One of the most critical yet often undervalued steps in developing conversational AI is crafting comprehensive technical specifications upfront. This post explores best practices and common pitfalls around documentation.

Developing a chatbot, voice assistant or other conversational agent is a complex undertaking that requires clear requirements right from the start. An inadequate technical specification leads to cascading issues down the road that impact budget, delivery timeline, product quality and more. We’d like to share some recommendations for writing solid specs that set your engineering team up for success.The most critical elements that good conversational AI documentation will define include:

Purpose Statement 

Articulate exactly what automated tasks or workflows the agent will handle. Use cases and scope need concrete detailing even if ideals shift later.

End User Profile 

Document target demographic and develop representative personas. These inputs help shape the dialogue flow, language register and more.

Functional Specifications 

Comprehensively catalogue intended capabilities and limitations based on business team vision as well as technical realities. Continually review and update if needs evolve.

Quantitative Benchmarks 

Establish key performance indicators for critical dimensions like response latency given anticipated simultaneous user volumes. This informs infrastructure requirements early.

External Integrations 

Identify all third-party platforms, services and databases the system will interface with together with complete API specifications. Solve integration problems proactively.

Technologies in Play 

Select preferred speech recognition and synthesis technologies to meet quality measures if incorporating an audio interface. Clarify any ML or NLP techniques leveraged under the hood.

Data Management Plan 

Map out protocols and controls regarding security, privacy, retention periods etc. for user information based on its classification level. Remain vigilant for compliance.

Conversational Personality 

Define a consistent identity that aligns with use case context. Things like bot name, gender, avatar image, voice specs and backstory lead to meaningful user connections over time.

Continuous Improvement 

Allocate resources for constant monitoring, machine learning retraining based on logs analysis and new intent development cycles essential for automating at scale. Treat launch as the beginning.

ConclusionIn closing, meticulously documenting the vision, requirements, risks, assumptions and constraints lays the groundwork for successfully building the right solution. It also allows adjusting scope if needs shift based on business realities and technological challenges. We advise teams to treat the technical specifications for AI assistants as living documents and revisit them actively rather than set in stone.