Technical Deep Dives

How do you know if your AI is good? Learn key metrics for evaluating classification, generation, and other AI tasks.

Chain multiple AI steps together into workflows. Learn orchestration patterns, error handling, and tools for building AI pipelines.

Fine-tuning adapts pre-trained models to your specific use case. Learn when to fine-tune, how it works, and alternatives.

RAG systems retrieve relevant context before generating responses. Learn retrieval strategies, ranking, and optimization techniques.

Semantic search finds results based on meaning, not exact keyword matches. Learn how it works and how to implement it.

Temperature, top-p, and other sampling parameters control how creative or deterministic AI outputs are. Learn how to tune them.

Vector databases store and search embeddings efficiently. Learn how they work, when to use them, and popular options.

Go beyond basic RAG: hybrid search, reranking, query expansion, HyDE, and multi-hop retrieval for better context quality.

Scale AI training across multiple GPUs and machines. Learn data parallelism, model parallelism, and pipeline parallelism strategies.

Compress AI models with quantization, pruning, and distillation. Deploy faster, cheaper models without sacrificing much accuracy.

Master advanced model compression: quantization-aware training, mixed precision, and distillation strategies for production deployment.

Fine-tune or train embedding models for your domain. Improve retrieval quality with domain-specific embeddings.

Train models that understand images and text together. Contrastive learning, vision-language pre-training, and alignment techniques.