Generating Specs via AI from Source Code: When AI Analyzes Your Codebase to Create Realistic Specifications

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

“Add Multi-Factor Authentication”

Monday morning. Release planning meeting.

Product Manager: “For the next release, we’re adding multi-factor authentication. It’s a priority customer request.”

The PM opens Notion and starts writing the spec:

# Capability: Multi-Factor Authentication (MFA)

**Objective:**
Allow users to secure their accounts with multiple authentication methods.

**Supported methods:**
- SMS with 6-digit code
- Authenticator app (TOTP)
- Hardware security key (FIDO2)
- Email with confirmation link

**User Stories:**
- As a user, I want to enable MFA in my settings
- As a user, I want to choose my preferred method
- As a user, I want to configure multiple backup methods

PM: “Spec done. Estimate?”

Lead Developer (who hasn’t read the current authentication code yet):

Dev Lead: “I’d say 1 sprint, maybe 2. I’ll assign to the team.”

3 days later.

Developer opens the authentication module source code.

# app/models/user.rb
class User < ApplicationRecord
  devise :database_authenticatable, :registerable

  # Current authentication: email + password ONLY
  # Architecture: only 1 auth method supported by devise
  # No multi-provider system
end

Developer escalates to Lead:

Dev: “We have a problem. Our current authentication architecture only supports one method: email + password.”

Dev Lead: “So what?”

Dev: “To support MFA, we need to completely refactor the authentication system. Devise doesn’t natively handle multiple simultaneous methods. We need to create a provider system, manage partial authentication states, implement the MFA flow…”

Dev Lead: “Estimated time?”

Dev: “3 weeks minimum. Plus 1 week of testing. The spec mentions 4 different methods. Each requires specific integration.”

Lead Developer goes back to the PM.

Dev Lead: “Your MFA spec… We can’t do this in 2 sprints.”

PM: “Why? It’s just adding an option in settings.”

Dev Lead: “No. It’s refactoring the entire authentication architecture. The current code only supports one method. Your spec asks for 4. We need to rework everything.”

PM: “But… I validated this spec with customers. It’s promised for the release.”

Dev Lead: “Should’ve analyzed the code before writing the spec.”

PM: “I don’t know how to code. How am I supposed to know your architecture doesn’t support this?”

Awkward silence.

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

The Problem of Specs Disconnected from Technical Reality

Most Product Owners and Product Managers write specs without knowing the real state of the code.

Legitimate goal: Define user needs and features to develop.

Frequent result: Technically impossible specs, underestimated, or requiring major unanticipated refactorings.

The Five Symptoms of Disconnected Specs

1. Impossible Specs (Current Architecture Doesn’t Allow It)

The Scenario:

PM writes an ambitious spec based on user needs.

Week 1: Spec validated by business. “Great feature!”

Week 2: Dev team starts technical analysis. “This is impossible with our current architecture.”

Week 3: Debate on how to refactor existing code. “This will take 6 weeks, not 2.”

Week 4: PM has to announce delay to customers. “Sorry, we underestimated the complexity.”

Real Example:

PM Spec: “Allow users to export their data in CSV, JSON, and Excel formats.”

Current code:

# app/services/export_service.rb
class ExportService
  def export_to_csv(data)
    # CSV export only
    # No multi-format handling
    # No abstraction to add other formats
  end
end

Reality: The export service is coupled to CSV format. To support JSON and Excel, the service needs complete refactoring, creating an abstraction, implementing each formatter.

Time estimated by PM: 3 days.

Real time: 2 weeks (refactoring + 3 formats + tests).

The Problem:

• ❌ PM doesn’t know current architecture
• ❌ Spec written without analyzing existing code
• ❌ Massive underestimation
• ❌ Dev frustration (“Another unrealistic spec”)

Quote from Lead Developer:

“PMs write specs as if we’re starting from scratch. But we have 200,000 lines of code with a specific architecture. Their specs never account for that.”

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

2. Underestimated Specs (“It’s Just Adding a Button”)

The Scenario:

PM thinks a feature is simple. “It’s just adding an export button.”

Hidden technical reality:

• Permissions: who can export?
• Limits: large exports crash the server
• Background jobs: asynchronous processing needed
• Notifications: email when export is ready
• Security: no access to other users’ data
• Tests: 15 edge cases to cover

PM estimation: 1 day.

Dev estimation after code analysis: 1 week.

The Problem:

• ❌ PM sees user interface, not technical complexity
• ❌ Specs don’t mention hidden constraints
• ❌ Misalignment between expectations and reality

Quote from Product Manager:

“I thought it was simple. How am I supposed to know I need to handle 15 edge cases if I don’t read the code?”

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

3. Specs Contradicting Existing Code (Architectural Conflicts)

The Scenario:

PM writes a spec that conflicts with current architecture.

Example:

PM Spec: “Users can create custom workflows with if/else conditions.”

Current code:

# app/models/workflow.rb
class Workflow < ApplicationRecord
  # Workflows = fixed linear sequence of steps
  # No conditions
  # No branches
  # Rigid architecture
end

Result: The spec asks for a conditional workflow system. Current code only handles linear sequences. Complete overhaul needed.

The Problem:

• ❌ Spec ignorant of architectural constraints
• ❌ Conflict between PM vision and technical reality
• ❌ Unplanned refactorings

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

4. Vague Specs (“Improve Performance”)

The Scenario:

PM writes generic spec without technical context.

PM Spec: “Improve dashboard page performance.”

Unanswered questions:

• Which parts of the page are slow?
• What’s the current load time?
• What’s the performance target?
• Which SQL queries are problematic?

Dev team must analyze code to understand:

• Profiling: identify bottlenecks
• Measurement: load time, number of SQL queries, render time
• Hypotheses: caching? N+1 queries? Lazy loading?

Time wasted: 2 days of analysis before even starting.

The Problem:

• ❌ Spec without technical context
• ❌ Dev must do analysis PM could have done with help
• ❌ Time wasted understanding the real problem

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

5. Specs Without Migration Context (“What About Existing Data?”)

The Scenario:

PM writes spec for new feature. Forgets existing data.

Example:

PM Spec: “Add ‘Payment Status’ field on orders. Possible values: Paid, Pending, Failed.”

Forgotten question: What do we do with the 50,000 existing orders in the database?

Technical reality:

• Data migration needed
• Backward compatibility logic (old code not handling new field)
• Deployment strategy (deploy code before or after migration?)

PM estimation: 1 day (add a field).

Dev estimation: 1 week (migration + backward compatibility + safe deployment).

The Problem:

• ❌ Spec ignores existing data and legacy code
• ❌ Underestimation of migration impacts

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

Why Specs Stay Disconnected from Reality

Reason 1: PMs Don’t Read Code (And That’s Normal)

The Problem:

Product Managers aren’t developers. They can’t read 200,000 lines of code to understand architecture.

Result: They write specs based on user understanding, not technical reality.

Quote from Product Manager:

“I understand user needs. But I don’t code. How am I supposed to know our authentication system only supports one method?”

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

Reason 2: Devs Discover Constraints Too Late

The Problem:

Dev team discovers technical constraints after the spec is written and validated.

Typical workflow:

1. Week 1: PM writes spec (no technical analysis)
2. Week 2: Spec validated by business
3. Week 3: Dev starts analyzing code (“Oh, this is impossible”)
4. Week 4: Debate on how to refactor
5. Week 6: Partial delivery, far from initial spec

Frustration on both sides:

• PM: “Why are you telling me this now?”
• Dev: “Why did you write this spec without consulting us?”

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

Reason 3: No Automated Source Code Analysis

The Problem:

There’s no tool to automatically analyze code and generate technical constraints for a spec.

Current workflow:

1. PM writes spec (business vision)
2. Dev reads spec
3. Dev manually analyzes code
4. Dev reports constraints
5. PM adapts spec
6. Back to step 2 (multiple iterations)

Time wasted: 1-2 weeks of back-and-forth.

Question: What if AI could analyze the source code before writing the spec?

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

The AI Approach: Analyze Code to Generate Realistic Specs

The Concept:

Use AI to analyze your codebase and generate specifications aligned with existing architecture.

How It Works

Step 1: AI Analyzes Source Code

Input: Your codebase (files, architecture, dependencies).

AI Analysis:

• Identify existing modules (authentication, payments, exports, etc.)
• Map architecture (services, models, controllers, jobs)
• Detect technical constraints (only one export format? linear workflows?)
• Identify code patterns (how features are currently implemented)

Output: Technical map of your codebase.

Example:

AI analyzes authentication module:

Code analysis: app/models/user.rb, app/controllers/sessions_controller.rb

Detected architecture:
- Gem used: Devise
- Authentication method: database_authenticatable (email + password only)
- Constraint: Devise doesn't natively support multiple simultaneous authentication methods
- Possible extensions: devise-two-factor (third-party gem for basic MFA)

Conclusion:
To implement MFA with multiple methods (SMS, TOTP, FIDO2), complete refactoring needed:
1. Create authentication provider system
2. Manage partial authentication states (email validated, awaiting MFA)
3. Implement each provider (SMS, TOTP, FIDO2)
4. Security tests (MFA bypass, timing attacks, etc.)

Estimation: 3-4 weeks (refactoring + implementation + tests)

Result: AI understands current architecture and identifies technical constraints.

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

Step 2: PM Describes Business Objective

PM Input: “I want to add multi-factor authentication to secure user accounts.”

AI combines:

• Business objective (MFA for security)
• Technical constraints (current Devise architecture)

AI generates realistic spec:

# Capability: Multi-Factor Authentication (MFA)

**Business objective:**
Secure user accounts with MFA.

**Identified technical constraint:**
Current architecture (Devise) only supports email + password.
For MFA, refactoring needed.

**Approach proposed by AI:**

Option 1 (Quick, Limited):
- Use `devise-two-factor` gem
- TOTP support only (no SMS, no FIDO2)
- Time: 1 week
- Limitation: only 1 MFA method supported

Option 2 (Complete, Long):
- Refactor authentication system
- Create multiple providers (SMS, TOTP, FIDO2)
- Support backup methods
- Time: 4 weeks
- Advantage: Flexible architecture for future methods

**AI Recommendation:**
Option 1 for MVP. Option 2 for complete version in next release.

**Generated issues:**
- [AUTH-101] Analyze devise-two-factor security
- [AUTH-102] Implement TOTP with devise-two-factor
- [AUTH-103] MFA settings UI
- [AUTH-104] Security tests (bypass, timing attacks)

Result: Realistic spec, aligned with architecture, with explicit options and trade-offs.

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

Step 3: Human Supervision (PO/PM/Dev Validate)

IMPORTANT: AI doesn’t replace humans. It assists.

Validation workflow:

1. AI generates spec based on code analysis
2. PM reads spec (“OK, AI detected technical constraints”)
3. Dev validates technical analysis (“Yes, correct. Devise doesn’t natively support multi-method MFA”)
4. PM chooses approach (“We’ll do Option 1 for MVP”)
5. Dev refines estimations (“1 week confirmed, I know devise-two-factor”)

Human keeps control:

• ✅ PM chooses approach (quick vs complete)
• ✅ Dev validates technical analysis
• ✅ Team decides priorities

AI provides:

• ✅ Automatic code analysis
• ✅ Technical constraint detection
• ✅ Realistic spec generation
• ✅ Time savings (2 days analysis → 10 minutes)

Quote from Product Manager:

“AI analyzed the authentication code in 2 minutes. Before, I’d ask a dev to do this analysis, it took 2 days. Now I have technical constraints before writing the spec.”

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

The Three Pillars of AI-Generated Specs

1. Automatic Source Code Analysis

The Concept:

AI scans your codebase to identify:

• Current architecture (patterns, frameworks, gems/packages)
• Technical constraints (only one format? linear workflows?)
• Dependencies (which parts of code are impacted by a change)
• Complexity (estimated time based on existing structure)

Advantages:

• No need to manually read 200,000 lines of code
• Automatic detection of hidden constraints
• Technical mapping in minutes

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

2. Spec Generation Aligned with Existing Code

The Concept:

AI generates a spec that:

• Respects current architecture (or proposes refactoring if needed)
• Identifies impacts (which files to modify, which dependencies to check)
• Proposes multiple approaches (quick vs complete, MVP vs final version)

Result:

• Realistic specs (no impossible promises)
• Explicit trade-offs (time vs features)
• Estimations based on real code (not intuitions)

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

3. Mandatory Human Supervision

The Concept:

AI assists. Human decides.

AI doesn’t:

• ❌ Choose technical approach for you
• ❌ Write code automatically without validation
• ❌ Make business decisions (MVP vs complete)

Human keeps control:

• ✅ Validate AI’s technical analysis
• ✅ Choose approach (quick, complete, hybrid)
• ✅ Adjust priorities based on business
• ✅ Revise generated spec if needed

Key principle:

Master AI and never be passive.

You use AI to save time on technical analysis. You remain master of decisions.

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

Real Example: TechOps Platform (SaaS Monitoring)

TechOps Platform (30-person team, infrastructure monitoring platform)

Note: TechOps Platform is a real company we’ve anonymized with a fictitious name to protect their confidentiality.

Before AI: Disconnected Specs, Endless Back-and-Forth

Configuration:

• Specs written in Notion by PMs
• Manual technical analysis by lead devs
• Back-and-forth: 1-2 weeks per spec

Problem Encountered:

PM Spec: “Allow users to export metrics in Prometheus, Grafana JSON, and CSV formats.”

Dev analysis (after 3 days):

# app/services/metrics_exporter.rb
class MetricsExporter
  def export(metrics)
    # CSV export only
    # No abstraction for multi-format
    # Prometheus and Grafana JSON = complex formats (time series, labels, metadata)
  end
end

Dev conclusion: “Need to refactor service, create formatter system, implement Prometheus and Grafana JSON. 3 weeks minimum.”

PM: “But I promised this to customers for the release in 2 weeks.”

Result: Release delayed by 4 weeks.

Quote from TechOps Product Manager:

“We wrote ambitious specs without knowing if they were technically feasible. Dev team discovered impossibilities after validation. It was frustrating for everyone.”

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

After AI: Code Analysis Before Spec

Workflow with AI:

1. PM describes objective: “I want to enable metrics export to Prometheus and Grafana.”

2. AI analyzes code:

Analysis: app/services/metrics_exporter.rb

Detected architecture:
- CSV export only
- No abstraction for multi-format
- Metrics stored as time series in PostgreSQL

Constraint:
Prometheus and Grafana JSON require:
- Time series → specific format conversion
- Labels and metadata handling
- Export service refactoring

Possible approaches:

Option 1 (Quick):
- Use `prometheus-client` gem for Prometheus export
- Implement Grafana JSON manually (simple format)
- Keep existing CSV export
- Time: 1 week

Option 2 (Complete):
- Refactor `MetricsExporter` into formatter system
- Implement Prometheus, Grafana JSON, CSV as formatters
- Extensible architecture for future formats
- Time: 3 weeks

3. AI generates spec:

# Capability: Multi-Format Export (Prometheus, Grafana JSON, CSV)

**Objective:**
Enable metrics export to Prometheus and Grafana.

**Technical constraint:**
Current export = CSV only. Refactoring needed.

**Recommended approach (Option 1 - MVP):**
- Prometheus export via `prometheus-client` gem
- Grafana JSON export (simple format)
- Keep existing CSV export
- Time: 1 week

**Issues:**
- [EXPORT-201] Implement Prometheus export
- [EXPORT-202] Implement Grafana JSON export
- [EXPORT-203] Export format selection UI
- [EXPORT-204] Format tests (Prometheus, Grafana, CSV)

**Future approach (Option 2 - Extensible architecture):**
Refactor into formatter system to ease adding new formats.
Planned for Q2 release.

4. PM validates spec (“OK, MVP in 1 week, complete version later”)

5. Dev confirms estimation (“1 week confirmed”)

Total time: 30 minutes (instead of 2 weeks back-and-forth).

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

Results (After 6 Months):

Metric 1: Technical analysis time

• Before: 2 weeks back-and-forth PM ↔ Dev to understand constraints
• After: 30 minutes (AI analyzes code automatically)

Metric 2: Unrealistic specs

• Before: 40% of specs required unanticipated refactorings
• After: 5% (AI detects technical constraints upfront)

Metric 3: Dev team satisfaction

• Before: Constant frustration (“PMs write impossible specs”)
• After: Smooth collaboration (“Specs are realistic”)

Metric 4: Time-to-market

• Before: Releases delayed by 30% on average (technical surprises)
• After: 95% of releases delivered on time

Quote from TechOps Lead Developer:

“Before, we wasted 2 weeks per spec explaining why it was impossible. Now AI analyzes code in 5 minutes and generates a realistic spec. We go straight to implementation.”

Quote from TechOps Product Manager:

“I don’t code. But with AI, I can write realistic technical specs. AI analyzes code, tells me what’s feasible, and I choose the approach. It’s revolutionary.”

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

Master AI, Never Be Passive

Fundamental principle:

AI is an assistant, not a decision-maker.

What AI Does Well

✅ Analyze 200,000 lines of code in minutes

• Identify current architecture
• Detect technical constraints
• Map dependencies

✅ Generate realistic specs based on code

• Propose multiple approaches (quick vs complete)
• Estimate complexity based on existing structure
• Identify impacts (which files to modify)

✅ Save time on technical analysis

• 2 weeks back-and-forth → 30 minutes
• No need to read code manually
• Automatic detection of hidden constraints

What AI Can’t Do (And Shouldn’t Do)

❌ Make business decisions for you

• Choose between quick MVP or complete version
• Define business priorities
• Decide trade-offs (time vs features)

❌ Write code without supervision

• AI can generate code, but humans must validate
• Risks: bugs, security flaws, unmaintainable code
• Principle: AI proposes, human validates and adjusts

❌ Understand complete business context

• AI analyzes code, not business constraints
• Example: “We must deliver in 2 weeks for priority customer”
• Only humans know these constraints

Golden Rules to Master AI

Rule 1: Always Validate Technical Analysis

Never blindly accept AI conclusions.

Workflow:

1. AI analyzes code and generates spec
2. Dev validates technical analysis (“Did AI correctly understand the architecture?”)
3. If validation ✅ → continue
4. If error ❌ → correct AI analysis

Example:

AI says: “Your system already supports MFA via Devise.”

Dev checks: “No, Devise is configured in basic mode. No MFA.”

Correction needed.

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

Rule 2: Human Chooses Approach

AI proposes multiple options. Human decides.

Example:

AI proposes:

• Option 1: Quick MVP (1 week)
• Option 2: Complete version (4 weeks)

PM decides: “We’ll do Option 1 now, Option 2 in next release.”

AI doesn’t choose. You choose.

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

Rule 3: Never Production Code Without Human Review

If AI generates code, humans must review it.

Risks of unreviewed AI code:

• Subtle bugs
• Security flaws (injection, XSS, etc.)
• Unmaintainable code
• Non-compliance with team conventions

Rule: AI accelerates. Human guarantees quality.

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

Rule 4: Supervise, Don’t Submit

You master AI. AI doesn’t master you.

Incorrect mindset:

“AI said we need to refactor. So we refactor.”

Correct mindset:

“AI identified that refactoring would be needed for this feature. Let’s analyze if it’s a priority. Maybe an MVP without refactoring is enough.”

Human keeps strategic control.

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

Sinra Integrates AI to Facilitate Spec Creation

At Sinra, we’re integrating AI to assist PO/PM/Dev in creating realistic specifications.

Objective: Specs Aligned with Real Code

The Concept:

When you create a capability or issue in Sinra, AI can:

1. Analyze your codebase (architecture, constraints, dependencies)
2. Generate realistic spec (aligned with existing code)
3. Propose multiple approaches (MVP vs complete)
4. Identify impacts (which files to modify, which dependencies to check)

Result: Realistic technical specs, even if you don’t code.

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

AI Features Being Integrated

Feature 1: Automatic Code Analysis for Capabilities

Use Case:

You create a capability “Add Prometheus export”.

AI:

1. Analyzes your current export code
2. Detects constraints (“CSV export only, refactoring needed”)
3. Generates spec with possible approaches (quick vs complete)

You validate and adjust.

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

Feature 2: Technical Issue Generation from Capability

Use Case:

You have a capability “Multi-Factor Authentication”.

AI:

1. Analyzes current authentication code
2. Generates necessary technical issues:
   • [AUTH-101] Refactor authentication system for multiple providers
   • [AUTH-102] Implement TOTP provider
   • [AUTH-103] Implement SMS provider
   • [AUTH-104] MFA settings UI
   • [AUTH-105] Security tests

You validate and assign.

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

Feature 3: Impact and Dependency Detection

Use Case:

You modify “Payments” module.

AI:

1. Detects all code parts depending on Payments module
2. Identifies potential impacts (“Modifying this service impacts 12 files”)
3. Generates issues to handle impacts

You avoid regressions.

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

Human Supervision Always Present

IMPORTANT: Sinra AI assists. You decide.

Sinra workflow with AI:

1. You create capability (“Prometheus export”)
2. AI analyzes code (detects technical constraints)
3. AI generates spec (with options and estimations)
4. You read and validate (“OK, I choose Option 1”)
5. Dev reviews technical analysis (“Correct”)
6. You create issues (manually or via AI)
7. Team implements (code reviewed by humans)

AI accelerates. You master.

Quote from Product Manager:

“Sinra + AI is like having a developer who analyzes code for me and tells me what’s feasible. But I remain master of my decisions.”

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

Five Signs Your Specs Are Disconnected from Code

Sign 1: Devs Often Say “It’s Impossible”

If dev team frequently responds “It’s technically impossible with our architecture”, your specs ignore real code.

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

Sign 2: Estimations Systematically Doubled

If your initial estimates are always doubled after technical analysis, your specs underestimate complexity.

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

Sign 3: Discovering Refactorings After Validation

If you discover refactoring needs after validating spec, you analyze too late.

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

Sign 4: Endless PM ↔ Dev Back-and-Forth

If you spend 2 weeks discussing technical feasibility, you lack automated analysis.

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

Sign 5: Releases Delayed by “Technical Surprises”

If your releases are delayed by unanticipated technical constraints, your specs are disconnected from code.

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

Action Points: Generate Realistic Specs via AI

1. Identify a capability to specify. Choose a complex feature.
2. Ask AI to analyze related code. (With Sinra or AI assistant of your choice)
3. Read generated technical analysis. Verify with a dev it’s correct.
4. Choose approach. Quick MVP or complete version? You decide.
5. Generate technical issues. Manually or via AI.
6. Implement with human supervision. Code reviewed by team.

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

The Key Point

Specs disconnected from code generate frustration, delays, and waste.

Between impossible specs, underestimations, surprise refactorings, and endless back-and-forth, PO/PM and Dev don’t speak the same technical language.

AI analyzes source code to generate realistic specs aligned with existing architecture.

The result:

• ✅ Automatic code analysis (architecture, constraints, dependencies)
• ✅ Realistic specs (no impossible promises)
• ✅ Explicit trade-offs (quick vs complete)
• ✅ Human supervision (you master AI, never passive)
• ✅ Time savings (2 weeks → 30 minutes)

Sinra integrates AI to facilitate realistic technical spec creation.

With human supervision. Always.

Master AI and never be passive.

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

Also discover the “Invisible Problems” series

• Dead Documentation - 127 obsolete Confluence pages: how to make documentation alive
• Living Glossary - Dead glossaries in Notion: how to use @pages for unified vocabulary
• Invisible QA - 847 tests in Excel: how to make QA visible with Sinra Testings
• Backlog Chaos - 537 chaotic issues: how to organize work by releases and cycles

url: /specifications/2026/01/23/ai-specs-generation-source-code.html

Ready to create realistic specs with AI? Discover Sinra →

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