OpenAI

Building LLM Applications

Tokens, context windows, prompting, structured outputs and function calling

Production RAG

Build retrieval systems that actually work in production

Building AI Agents

Give LLMs tools, memory and the ability to act autonomously

LLM Evaluation

Measure retrieval, generation, agents and production quality

Building Multimodal Applications

Vision, audio, image generation and cross-modal AI systems

Call an LLM with Structured Output

Implement call_structured() that returns a validated Pydantic model from an LLM, with retry on validation failure.

Embeddings & Vector Search

Implement a VectorStore with embed, insert, and search(filter) methods.

Build a ReAct Agent Loop

Implement run_agent(task, tools, max_iter) that loops reasoning + tool calling until final answer or max_iter.

Build an Evaluation Pipeline

Implement run_eval(golden_set, prompt_version) -> Report with faithfulness and answer_relevance scores.

AI Chatbot

Your first production LLM application

Document Summariser

Summarise long documents with chunking and map-reduce

RAG Assistant

Production retrieval-augmented generation over your documents

Research Agent

Autonomous multi-step research with tool calling