Analyze how generation parameters and sampling techniques shape model output. Develop foundational skills by tuning temperature, top-p, max tokens, and stop sequences, and by streaming responses while tracking token usage. Build practical capabilities by producing schema-valid JSON with structured outputs and exposing Python functions through tool calling.

Differentiate LangChain and LlamaIndex by examining chains, agents, tools, memory, and document indexing. Develop foundational skills by composing LCEL chains, integrating tools, managing conversation memory, and indexing documents with LlamaIndex. Build practical capabilities by combining both frameworks and justifying framework choices for application architectures.

Analyze how embeddings represent meaning and how vector databases enable similarity search. Develop foundational skills by generating embeddings, measuring semantic similarity, and managing data in ChromaDB, Pinecone, and Weaviate. Build practical capabilities by tuning nearest-neighbour indexes and selecting embedding models and vector databases for specific use cases.

Analyze RAG architectures by examining document loaders, chunking strategies, and retrieval chains. Develop foundational skills by building retrievers and retrieval chains with effective chunking. Build practical capabilities by designing grounded prompts with citations and safe no-answer responses, and by diagnosing retrieval failures.

Evaluate the end-to-end LLM application lifecycle by examining how API integration, orchestration, and retrieval combine in a working system. Develop foundational skills by integrating provider APIs, tuning generation parameters, and building multi-step chains. Build practical capabilities by designing and scoping a tool-using, retrieval-backed LLM application.