7 posts · page 1 of 1
Compare text-embedding-3-small/large, Cohere embed-v3, and sentence-transformers on dimensions, cost, MTEB scores, and practical RAG performance.
Decide between prompt engineering, retrieval, and fine-tuning by weighing cost, latency, control, and data requirements honestly.
Stream tokens from an LLM as they are generated to cut perceived latency, handle partial outputs, and build responsive chat UIs.
Understand how temperature and top-p sampling shape the creativity, determinism, and quality of large language model outputs.
Learn how tokens are counted, how to estimate API spend before you send a request, and concrete strategies to cut LLM bills without hurting quality.
Understand how tool calling lets LLMs invoke functions, why agents loop over tools, and how to design reliable tool schemas.
A working tour of the OpenAI Python SDK: chat completions, streaming, structured output, embeddings, tool calls, and production-grade error handling.