The Generative Operations Learning Foundation (GOLF) exists to provide practical, honest information about generative AI, machine learning operations, and AI adoption in business contexts.

We’re not selling AI solutions or promoting specific vendors. We’re documenting what actually works, what doesn’t, and what organisations need to know when implementing AI systems.

Why GOLF Exists

The generative AI space is full of hype, vendor marketing, and unrealistic expectations. Companies are being told AI will transform everything overnight. Consultants are selling solutions to problems organisations don’t fully understand yet.

The reality is more complicated. Generative AI has genuine capabilities and real limitations. MLOps practices matter more than model selection. Successful implementation depends more on process and culture than technology.

GOLF aims to cut through noise and provide information useful for people actually implementing these systems.

What We’ll Cover

Generative AI fundamentals: How large language models work, what they’re good at, what they can’t do, how to evaluate capabilities realistically.

MLOps practices: Managing model development, deployment, monitoring, and maintenance in production environments.

Enterprise AI adoption: Organizational challenges, change management, skill development, realistic timelines and expectations.

Tool and platform comparisons: Honest assessments of commercial and open source options based on actual use cases, not marketing claims.

Case studies: Real implementations, including failures and mistakes, not just success stories.

Technical deep dives: For practitioners who need detailed information about specific techniques, architectures, or approaches.

Who This Is For

Data scientists and ML engineers implementing AI systems.

IT leaders and technical managers deciding on AI strategy and tooling.

Business stakeholders trying to understand what’s realistic vs. what’s marketing hype.

Anyone working on AI projects who wants practical information instead of promotional content.

Our Approach

We don’t assume AI is the answer to everything. Sometimes traditional software or business process changes work better than AI solutions.

We acknowledge failure and mistakes. Learning from what doesn’t work is as valuable as showcasing successes.

We focus on practical implementation details. Theory matters, but we’re more interested in what actually works in production.

We stay vendor-neutral. We’ll discuss specific tools and platforms, but our goal is honest assessment, not promotion.

The Current State of Generative AI

As of March 2026, generative AI has matured somewhat from the initial ChatGPT hype cycle, but challenges remain.

What works well:

• Text generation for drafting, summarization, and transformation tasks
• Code assistance and generation for common patterns
• Content analysis and classification
• Conversational interfaces for specific, well-defined domains

What’s still challenging:

• Reliability and consistency in production systems
• Cost management at scale
• Handling edge cases and preventing harmful outputs
• Integration with existing business systems
• Measuring actual ROI beyond pilot projects

What’s overhyped:

• Claims of AI replacing entire job functions
• Promises of perfect accuracy or human-level reasoning
• Suggestions that AI implementation is simple or quick
• Marketing around “AGI” or human-level AI (we’re nowhere close)

MLOps Maturity

Many organizations implementing generative AI lack basic MLOps practices. They can run models but can’t deploy them reliably, monitor them effectively, or update them safely.

MLOps isn’t glamorous, but it’s essential for anything beyond demos and prototypes. We’ll cover:

• Version control for models, data, and code
• CI/CD pipelines for ML systems
• Monitoring model performance and data drift
• Managing model deployment across environments
• Handling retraining and model updates
• Cost optimization and resource management

AI Education Needs

The skills gap in AI is real but often misunderstood. Organizations don’t primarily need more data scientists. They need:

• Engineers who understand ML systems in production
• Product managers who can scope realistic AI projects
• Business leaders who understand capabilities and limitations
• Data engineers who can build reliable pipelines
• DevOps engineers who can deploy and monitor ML systems

Training existing staff is often more effective than hiring AI specialists who don’t understand your domain.

What’s Coming

Our first proper post will examine prompt engineering - what actually matters vs. what’s superstition. There’s a lot of questionable advice out there about “prompt engineering best practices” that don’t hold up to testing.

After that, we’ll cover model selection criteria. How do you choose between GPT-4, Claude, Gemini, or open source alternatives for specific use cases? What actually matters in that decision?

Then we’ll get into MLOps fundamentals - the unglamorous but essential infrastructure for production AI systems.

Get Involved

This is intended to be a collaborative resource. If you’re working on AI implementation and have experiences to share (successful or otherwise), reach out.

If you’ve got specific questions or topics you’d like us to cover, let us know. We’re more interested in addressing real needs than following a predetermined content plan.

Acknowledgments

GOLF draws on the work of many practitioners, researchers, and organizations doing serious work in AI. We’ll cite sources and give credit where it’s due.

We’re also grateful to the many people who’ve shared their implementation experiences, mistakes, and lessons learned. Learning what doesn’t work is often more valuable than success stories.

Moving Forward

Generative AI and MLOps are evolving rapidly. What’s true today might change in six months. We’ll try to keep content updated and note when information becomes outdated.

The goal isn’t to have perfect information, but to have honest, practical information that helps people make better decisions and implement more effective AI systems.

Let’s get started.