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In this second article in our short series on SLM optimization techniques we focus on the reuse of the prompt prefix with a key-value cache.

This article covers five prompt optimization strategies such as: prompt optimization, prompt engineering, LLM output quality, few-shot prompting, chain-of-thought, structured outputs.

See how SerpApi's Markdown output can cut search-result token usage by up to 74%, reducing AI agent costs and context-window overhead.

This article walks through how ChatGPT Work specifically earns its reputation, where the underlying models genuinely hold up against the competition, and where the honest limits are.

Explore five free hands-on workshops covering data engineering, machine learning, MLOps, LLMs, AI agents, and AI development through practical lessons, homework, projects, and community-based learning.

Scikit-LLM wraps language models in the scikit-learn estimator API, so it drops into a Pipeline or a cross-validation loop natively.

Learn how to build effective evals for AI agents, from designing clear tasks and choosing the right graders to building reliable eval harnesses and tracking changes over time.

Explore how JONI approaches AI agent orchestration with persistent runtimes, multi-model routing, execution capabilities, and reliability beyond simple content generation.

Opal is Google Labs' no-code tool for turning natural language into working AI mini-apps, built on top of an internal framework called Breadboard, and created by Google engineer Dimitri Glazkov.

Explore five free Microsoft GitHub courses covering data science, machine learning, artificial intelligence, generative AI, LLMs, RAG, fine-tuning, and AI agents.