24 Weeks of AI-Native SaaS: A Developer's Learning Journey

Phase 1: The AI Integration Layer (Weeks 1–8)

Focus: Understanding how applications interact with LLMs and building reliable AI features.

Week 1: Multi-Model Evaluator.
Build a side-by-side comparison tool for GPT, Claude, and Gemini to understand response differences and trade-offs.
Week 2: Local RAG (Retrieval-Augmented Generation).
Create a system to chat with local documents using embeddings, vector search, and citation-based answers.
Week 3: Structured Data Processing.
Convert unstructured inputs (emails, receipts) into validated JSON using schema-based parsing.
Week 4: Voice-to-Workflow Pipeline.
Transform voice input into structured tasks such as tickets or notes.
Week 5: LLM Evaluation Patterns.
Use one model to evaluate or validate outputs from another model.
Week 6: Multimodal UI Analysis.
Analyze screenshots and generate structured UI/UX feedback.
Week 7: Usage & Cost Tracking.
Monitor token usage and estimate cost across different AI interactions.
Week 8: Phase 1 Consolidation.
Combine all components into a unified demo application.

Focus: Moving from simple responses to task-oriented AI workflows.

Week 9: Autonomous Research Agent.
Gather information from multiple sources and generate structured summaries.
Week 10: Natural Language to SQL.
Convert user queries into executable database queries.
Week 11: Multi-Agent Collaboration.
Simulate different roles (e.g., developer, reviewer) working together.
Week 12: Long-Term Memory (Graph-Based).
Store and retrieve relationships between entities over time.
Week 13: Automated Code Review.
Analyze pull requests for issues, improvements, and best practices.
Week 14: Human-in-the-Loop Systems.
Introduce approval steps before executing AI-generated actions.
Week 15: Local Model Execution.
Run models locally to understand privacy-focused setups.
Week 16: Phase 2 Consolidation.
Combine agent workflows into a unified system.

Focus: Building reliable, scalable, and safe AI systems.

Week 17: Input Safety & Validation.
Detect and handle unsafe or malicious inputs.
Week 18: Vector Data Management.
Manage and migrate embeddings across systems.
Week 19: Model Adaptation.
Fine-tune smaller models for domain-specific tasks.
Week 20: Scalable Deployment.
Deploy AI services using serverless or scalable infrastructure.
Week 21: Observability & Debugging.
Track system performance, failures, and latency.
Week 22: Model Routing.
Dynamically choose models based on task complexity.
Week 23: Output Safety & Compliance.
Detect sensitive or restricted outputs before returning responses.
Week 24: Final Integration.
Combine all components into a complete AI-powered application.