Supported by
The Generative Operations Learning Foundation is supported by Team400, an Australian AI consultancy helping organisations adopt generative AI responsibly.
Generative AI education and operations
Practical generative AI knowledge for Australian organisations.
From prompt engineering to MLOps pipelines, we publish clear guides on building and running generative AI systems in production.
What we cover
- Generative AI models and architectures
- MLOps and LLMOps pipelines
- Prompt engineering and fine-tuning
- AI governance and responsible deployment
What you can expect
- Hands-on tutorials and walkthroughs
- Production deployment case studies
- Model evaluation and benchmarking guides
- Cost optimisation strategies for AI workloads
Latest posts
View all-
Prompt Engineering for LLMs: What Actually Works in 2026
Prompt engineering techniques range from genuinely useful to cargo cult nonsense. Here's what reliably improves LLM outputs based on testing, not hype.
-
Vector Databases: When You Actually Need One
Vector databases are marketed as essential AI infrastructure. For many use cases, simpler solutions work better. Here's when vector DBs genuinely add value.
-
Fine-Tuning vs Few-Shot Prompting: When Each Actually Makes Sense
Fine-tuning gets talked about more than it gets used properly. For most applications, few-shot prompting or RAG work better. Here's when fine-tuning is worth it.
-
LLM Context Windows: The Practical Limits Nobody Talks About
Models advertise 100K+ token context windows, but performance degrades significantly with very long contexts. Here's what actually works in production.
-
What Australian Universities Are Getting Wrong About AI Education
Computer science programs are producing graduates who can train models but can't deploy them. The curriculum gap is hurting both students and employers.
-
Synthetic Data for LLM Training: What's Working and What Isn't
Using model-generated data to train other models sounds circular. But synthetic data approaches are producing real results when done carefully.
-
LLM Fine-Tuning: When It's Actually Necessary (and When Prompting Is Enough)
Fine-tuning large language models is expensive and complex. For many use cases, better prompting, retrieval-augmented generation, or few-shot examples work just as well.
-
Model Versioning in Production MLOps: Beyond Git and DVC
Versioning trained models requires tracking not just model files, but training data, hyperparameters, dependencies, and evaluation metrics. Here's what actually works at scale.
-
The Best AI Certifications for Australian Professionals in 2026
AI certifications are multiplying fast. Here's a practical breakdown of which ones matter for Australian professionals, what they cost, and what employers value.
-
Understanding Transformer Architecture Without the PhD
Transformers power every major language model today. Here's how they actually work, explained in plain language without the academic jargon.
-
Model Drift Detection: When to Retrain and When to Debug
Production ML models degrade over time as data distributions shift. Detecting drift early is crucial, but not all drift requires retraining—sometimes it reveals data quality issues.
-
LLM Prompt Injection Attacks: Why Traditional Input Validation Doesn't Work
Prompt injection exploits are fundamentally different from SQL injection or XSS. The defenses that work for traditional attacks don't translate well to language models.
-
Detecting and Mitigating LLM Hallucinations in Production Systems
Large language models generate plausible but incorrect information with concerning frequency. Effective production deployments require systematic hallucination detection and mitigation strategies.
-
Vector Database Scaling: What Happens When Embeddings Hit Production Scale
Vector databases enable semantic search and RAG applications, but scaling to millions or billions of vectors introduces performance and cost challenges that prototypes don't reveal.
-
LLM Inference Cost Optimization: Strategies That Work
Running large language models in production gets expensive fast. These optimization strategies reduce costs without sacrificing response quality.