Use Case Discovery & Feasibility

Identify the problem space and evaluate where LLMs can provide tangible value.

• Business use case definition
• LLM suitability assessment
• Data availability check
• Success metrics outline

Data Strategy & Curation

Design the data pipeline and gather domain-relevant datasets for fine-tuning or prompt engineering.

• Dataset collection & preprocessing
• Annotation (if needed)
• Privacy & compliance review
• Data quality checks

Model Selection & Architecture Planning

Choose the right model (e.g., GPT, LLaMA, Mistral) and define how it integrates with your system.

• Model selection (open-source or API-based)
• Fine-tuning vs. prompt engineering decision
• Infrastructure and compute planning
• Architecture diagram

Prototype Development

Build a functional prototype to validate LLM behavior, user interaction, and system fit.

• MVP with core functionality
• Prompt design / fine-tuning setup
• Integration with frontend/backend
• Initial user testing

Testing & Optimization

Evaluate model performance, hallucination rate, latency, and overall user experience.

• Evaluation metrics (accuracy, relevance, safety)
• Prompt refinement or model retraining
• Feedback loop setup
• Performance tuning

Deployment & Integration

Launch the LLM application within your environment, ensuring security and scalability.

• Cloud/on-prem deployment
• API endpoints & UI integration
• Logging, monitoring, and fallback logic
• Security & access controls

Post-Launch Support & Iteration

Monitor system behavior, gather user feedback, and continuously improve the application.

• User feedback analysis
• Continuous prompt/model updates
• Maintenance and performance tracking
• New feature rollouts