Writing Detailed Specifications with AI: From Configuration to Documentation
Writing Detailed Specifications with AI: From Configuration to Documentation
Every successful feature starts with a specification that is understood consistently by everyone involved. In banking transformation projects, however, maintaining accurate and synchronized documentation is a challenge. Use cases, screen specifications, integration documents, data mappings, and business requirements evolve continuously, while multiple teams and stakeholders contribute to the solution.
Traditionally, keeping documentation aligned required significant manual effort. Changes made in one area often had to be reflected across multiple documents, creating a risk of inconsistencies, outdated content, and duplication. The challenge becomes even greater when clients require their own documentation standards and templates.
A Real-World Challenge
During a Retail and Corporate Loan Origination implementation for a leading CEE bank, multiple vendors contributed to the overall solution. To ensure consistency across workstreams, the bank managed all specifications within its own Confluence environment and required vendors to follow standardized templates.
For ApPello, this meant producing documentation that met external standards while maintaining internal efficiency and quality. AI became a key enabler in achieving both objectives.
Why AI Works Particularly Well at ApPello
ApPello's platform is built as a highly configurable low-code/no-code solution. Screens, business rules, workflows, validations, user rights, and calculations are defined through structured configuration rather than hardcoded logic.
This architecture creates an ideal foundation for AI-assisted documentation.
Because configuration, source code, and technical documentation are maintained in Git, AI can access not only business requirements and existing specifications but also the actual implementation details behind the solution. Rather than guessing how a feature works, the AI can interpret the configuration directly and generate documentation based on the real system behavior.
This is a fundamental difference from using a generic AI tool to draft documents.
Specification, Configuration, and Code in One Place
This is possible because specifications do not live apart from the system they describe. They are maintained alongside configuration and source code within the same Git repository.
This approach creates a single source of truth where AI can access:
Specifications
Configuration
Source code
as one connected solution.
As a result, AI can identify situations where documentation and implementation have diverged. Examples include:
Documented processes no longer reflected in configuration
Implemented functionality missing from documentation
Validation rules described in specifications but not implemented
Instead of discovering these discrepancies during testing or production, teams can identify and resolve them much earlier during the implementation.
How the Process Works
ApPello has established documentation standards for all specification types. AI applies these standards consistently, ensuring that every deliverable follows the same structure, terminology, and level of detail.
The AI can use multiple sources simultaneously:
Business requirements
Meeting notes
Existing specifications
Platform configuration
Source code
Product capability descriptions
By combining these inputs, it creates documentation that reflects the complete solution rather than a single source document.
The process supports both creating new specifications and updating existing ones. When clients require their own templates, documentation can be generated or converted into the required format with minimal effort.
In the mentioned CEE banking project, AI-assisted workflows generated and updated specification content in the formats required by the client's Confluence environment, keeping the documentation aligned with both client and internal standards.
From Configuration to Documentation
In a typical flow, the specification comes first and the configuration follows from it. In practice, however, the configuration is sometimes the starting point, and when it is, AI can generate the specification directly from it. Let’s see some examples for such situations! In prototyping workshops, we configure the system together with the client and let the AI prepare the specification from the result, so that what was agreed in the room is captured immediately and accurately. In other cases we ask the AI to configure the system from the business requirements first and then generate the functional specification from that configuration, producing a working setup and its documentation in a single pass.
When documenting a screen, the AI receives the actual screen configuration and generates a complete specification, including:
User interactions
Business rules
Preconditions
Alternative flows
Available operations
Validation logic
The resulting documentation reflects the system's actual behavior rather than a manual interpretation of it.
Subject Matter Experts can further enrich the specification through natural language input. Business Analysts and developers simply describe important business scenarios, edge cases, or implementation details, which are automatically incorporated into the generated documentation.
Keeping Documentation Alive
One of the biggest challenges in implementation projects is documentation drift.
As systems evolve, specifications often become outdated because updates require manual effort. AI changes this dynamic.
Integrated with GIT, Confluence and Jira, AI can update existing specifications whenever requirements change. Instead of rewriting documents, teams provide instructions, and the AI updates relevant sections while preserving structure and consistency. When multiple documents are affected, updates can be performed across all impacted specifications simultaneously.
The role of Business Analysts does not disappear but evolves.
Rather than spending time on document generation, analysts focus on reviewing content, validating business context, identifying missing scenarios, and ensuring the documentation remains concise and relevant. AI generates; experts validate.
AI-Assisted Configuration
The same principles extend beyond documentation.
ApPello's visual configuration tools allow users to define screens, workflows, permissions, and business rules through configuration. While individual configuration tasks are straightforward, they often involve repetitive setup activities.
With AI assistance, Business Analysts can describe the desired functionality, and the AI performs much of the configuration work automatically.
This accelerates delivery, reduces repetitive effort, and makes advanced platform capabilities more accessible to less experienced team members.
The four-eyes principle remains unchanged: every AI-generated configuration is reviewed and approved by a second person before implementation.
Key Takeaways
Our experience shows that the value of AI depends largely on the quality of the context available to it. Because ApPello's platform was designed around structured configuration, comprehensive documentation, and version-controlled storage, AI can operate with precision rather than approximation.
The result is faster specification creation, easier maintenance, improved consistency, and closer alignment between documentation and implementation.
Most importantly, AI does not replace expert judgment. It removes repetitive work and allows Business Analysts, architects, and developers to focus on the decisions that create real value.
In the next article in this series, we will explore how the same principles can be applied to software development itself and how AI supports coding activities across the delivery lifecycle.