GENERATIVE AI

The lifecycle of a generative AI project typically involves several key phases, from initial planning to deployment and continuous improvement. A high level outline of the generative AI project lifecycle is outlined below:

Problem Definition & Scoping

Vision & Goals

Define Use Case

Stakeholder Buy-In
Model Selection

Data Collection & Preparation

Select Architecture

Model Selection [Pre-Trained vs. Custom]
Adapt & Align

Prompt Engineering

Fine-Tuning

Evaluation
Application Integration

Optimize & Deploy Model for Inference

Augment Model & Build LLM-Powered Applications

A Generative AI Readiness Assessment is a structured approach for evaluating an organization's preparedness to implement and integrate generative AI technologies effectively. This assessment typically involves analyzing technical capabilities, organizational readiness, ethical considerations, and potential use cases.

A readiness assessment may be summarized as follows:
Strategic Objectives

Vision and Goals

Use Case Identification

Stakeholder Buy-In
Technical Infrastructure

Data Availability and Data Quality

Cloud and Computing Resources

Integration Capability
Workforce Readiness

Skill Levels

Training Programs

Change Management
Ethical and Regulatory Considerations

Bias and Fairness

Data Privacy

Transparency

Security

Reliability

Dependency Risks
Pilot & Evaluation Framework

Pilot Projects

Performance Metrics

Feedback Loops
Vendor and Tool Selection

Tool Suitability

Vendor Reliability

Open Source vs. Proprietary