If you want to learn complete AI Engineering stack, follow these stages: Stage 1: How LLMs actually work Tokens, context window, embeddings, prefill vs decode, why every extra token costs time and
Why CEREBRO kept it
LLM fundamentals: tokens, context, prefill/decode mechanics.
The text below is an automated extraction of the article at https://x.com/kmeanskaran/status/2104077832393801757, stored verbatim in the public cerebro-vault repository. Copyright remains with the original publisher (x.com).
Stage 1: How LLMs actually work Tokens, context window, embeddings, prefill vs decode, why every extra token costs time and
> LLM fundamentals: tokens, context, prefill/decode mechanics.
If you want to learn complete AI Engineering stack, follow these stages:
Stage 1: How LLMs actually work Tokens, context window, embeddings, prefill vs decode, why every extra token costs time and money.
Stage 2: Prompts and APIs System prompts, JSON output, tool schemas, retries, prompt versions. Treat prompts like production code.
Stage 3: Retrieval (RAG) Chunk docs, search, rerank, keep data fresh, cite sources, rewrite bad queries. Most “hallucinations” are retrieval failures.
Stage 4: Backend for GenAI FastAPI, microservices, auth, rate limits, request queues, Redis caching, web
Backlinks
Appeared in 1 briefing