Deep dives, benchmark analysis, and engineering guides to help you minimize token footprints, design efficient prompts, and cut API spend.
Learn step-by-step strategies to shrink GPT-4 prompt sizes by removing filler words, optimizing context structures, and preserving key instructions.
Dive deep into Byte-Pair Encoding, how tiktoken handles punctuation, and why simple spacing and uppercase letters can unexpectedly double your API bills.
System prompts are sent on every single API request. Learn how to design a high-efficiency system prompt that cuts bloat and maximizes context space.
Anthropic recommends using XML tags for structure. Discover how to use XML tags efficiently without wasting precious input tokens in multi-shot configurations.
Is it cheaper to provide 10 few-shot examples in every prompt or to train a custom fine-tuned model? We analyze the mathematical crossover threshold.
Understand the structural differences between OpenAI's cl100k_base/o200k_base, Meta's Llama tokenizer, and Google's Gemini tokenizer.
Context caching is now supported by Anthropic and Google Gemini. Learn how to cache large system guides or books to slash repeat call charges.
LLMs do not require polite greetings or redundant explanations. Master the art of extreme prompt compression through these 10 linguistic rules.
Retrieval-Augmented Generation (RAG) is a massive token hog. Learn how reranking, metadata filtering, and chunk size controls can keep costs in check.
Multi-agent frameworks like Autogen or CrewAI generate hundreds of recursive queries. Here is how to budget, cache, and throttle your agent loops.