Step-by-step tutorials on building voice and chat AI agents using OpenAI Agents SDK, Realtime API, function calling, multi-agent orchestration, and production deployment patterns.
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Understand context windows in LLMs — what they are, how they differ across models, and practical strategies for building applications that work within token limits.
A clear, code-driven explanation of the transformer architecture including self-attention, multi-head attention, positional encoding, and how encoder-decoder models work.
Understand the autoregressive generation process, KV cache optimization, batching strategies, and the latency vs throughput trade-offs that govern LLM inference performance.
Learn the complete LLM training pipeline from pre-training on internet-scale data through supervised fine-tuning and RLHF alignment, with practical code examples at each stage.
A practical comparison of the major foundation models — GPT-4, Claude, Gemini, Llama, and Mistral — covering capabilities, pricing, context windows, and guidance on when to use each.
Learn what embeddings are, how they capture semantic meaning as vectors, how to use embedding models for search and clustering, and the role cosine similarity plays in AI applications.
Build maintainable prompt systems using Jinja2 templates, Python f-strings, and variable injection. Learn how to version control prompts and create dynamic instruction pipelines for production AI applications.
Master the fundamentals of prompt engineering — learn to write clear system and user messages, format instructions for consistency, and avoid common pitfalls that cause unreliable LLM outputs.
