Design · Documentation

SRS — Documenting every process from start to finish

Structured knowledge is the competitive edge.

Without a thorough knowledge base, AI gives generic answers or hallucinates; with solid documentation it becomes a surgical-precision copilot.

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The principle

Documenting every process is the real competitive advantage.

Teaching AI the exact structure of company documents, turning it into a Custom Skill or a structured System Prompt, and integrating it into the analysis and drafting of the SRS (Software Requirements Specification) optimizes the entire software lifecycle.

01 · Skill & Auditor

AI as a “Skill” and documentation Auditor

Instead of asking it to “write a requirements document”, we give it the company's canonical skeleton (the SRS template) so it works inside well-defined boundaries.

User input (raw dump)notes, recordings, constraints
AI as a SkillSRS template
AI as an Auditorgap analysis
Final SRSvalidated

3 The three key functions

Input normalization

Turns messy notes, meeting transcripts and stakeholder wishes into formal requirements.

Gap analysis

Highlights logical gaps, unhandled edge cases, forgotten error scenarios and concurrency issues.

Compliant formatting

Every section follows the company syntax: User Stories (As a… I want to… So that…), input/output tables for APIs, security specifications.

System Prompt

The Skill skeleton

Ruolo: Business Analyst senior.
Compito: trasforma il testo grezzo in un SRS.
Struttura obbligatoria:
  1. Obiettivi e KPI
  2. Personas, ruoli, permessi
  3. User Stories (As a... I want... So that...)
  4. API: tabelle input/output
  5. Requisiti non funzionali
Regole: non inventare dati; se manca
un'informazione, scrivi "DA CHIARIRE".
Output finale: elenco delle lacune.

Sample finding

AI as a strict QA Manager

Requirement“The system must generate PDF reports.”

GapThe maximum acceptable time for a report with over 10,000 rows is missing.

02 · SRS structure

Three levels of requirements

A modern SRS developed with AI support is organized on three fundamental levels.

A · The “what” and the “why”

Analysis and business

Business goals and KPIs
User profiles (personas), roles and permissions
Use Cases and User Stories with pre/post-conditions and alternative flows

B · The “how”

Technical and architectural

Technology stack and infrastructure
API specifications and integrations: REST/gRPC, data contracts, webhooks
Data model: ER, transactions, retention

C · Often forgotten

Non-functional and constraints

Performance, security, reliability, scalability
Guarded by automatic checklists
Every gap becomes a question for the client

C Non-functional requirements: what AI guards

NFR category	Topics covered	Example gap identified by AI
Performance	Response times (latency), throughput (RPS), startup time	“The maximum acceptable loading time for a PDF report with over 10,000 rows is missing.”
Security	Authentication (OAuth2/MFA), encryption in transit and at rest, GDPR	“Retention and anonymization policies for personal data after account deletion are not specified.”
Reliability	SLA/SLO, disaster recovery, API failure handling	“What happens to the system if the external payment gateway times out?”
Scalability	Auto-scaling, caching (Redis/CDN), database partitioning	“The estimated maximum load during seasonal traffic peaks is missing.”

03 · Workflow

From requirements gathering to approval

01
Raw context ingestion
Meeting minutes, analysis emails and the client's known constraints are loaded into the AI.
02
SRS draft generation
The AI applies the structured template and produces the first complete version.
03
Automatic stress test
An audit prompt asks it to act as a strict Lead Architect or QA Manager: contradictions, inconsistent terminology, unmeasurable requirements.
04
Human validation and refactoring
The Solution Architect or Business Analyst reviews the findings, fills in missing information and approves.
05
Derived artifacts
From the final SRS: epics and tickets for Jira or GitHub, test cases for QA, API documentation drafts.

DigitalSolutions

Document well, and AI works better.

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