AI/LLM Cheatsheet 01 — LLM Basics
Cheatsheet: API basics, tokens, costs, model choices.
LLM
AI/LLM Cheatsheet 02 — Prompt Engineering
Cheatsheet: prompt patterns, system messages, few-shot, CoT, role play.
AI/LLM Cheatsheet 03 — Tool Use / Function Calling
Cheatsheet: tool schemas, OpenAI + Anthropic patterns, parallel.
AI/LLM Cheatsheet 04 — Embeddings
Cheatsheet: embedding APIs, dimensions, normalization, similarity, chunking.
AI/LLM Cheatsheet 05 — RAG Patterns
Cheatsheet: RAG pipeline, chunking, retrieval, rerank, citations.
AI/LLM Cheatsheet 08 — Streaming Patterns
Cheatsheet: SSE setup, fastapi streaming, frontend EventSource.
AI/LLM Cheatsheet 09 — Function Calling Patterns
Cheatsheet: function calling patterns and gotchas.
AI/LLM Cheatsheet 10 — Evaluation
Cheatsheet: eval frameworks, golden datasets, LLM-as-judge.
AI/LLM Cheatsheet 11 — Cost Optimization
Cheatsheet: prompt caching, batching, model selection, output limits.
AI/LLM Cheatsheet 12 — Local LLMs (Ollama, vLLM)
Cheatsheet: Ollama, vLLM, llama.cpp, when to self-host.