Prompt

Why Care About Prompt Caching in LLMs?

, we’ve talked lots about what an incredible tool RAG is for leveraging the facility of AI on custom data. But, whether we're talking about plain LLM API requests, RAG applications, or more complex...

Claude Skills and Subagents: Escaping the Prompt Engineering Hamster Wheel

For those who’ve been constructing with LLMs for some time, you’ve probably lived through this loop again and again: you are taking your time crafting an important prompt that results in excellent results, after...

The Death of the “Every thing Prompt”: Google’s Move Toward Structured AI

been laying the groundwork for a more structured option to construct interactive, stateful AI-driven applications. One in all the more interesting outcomes of this effort was the discharge of their latest Interactions API...

Prompt Fidelity: Measuring How Much of Your Intent an AI Agent Actually Executes

Spotify just shipped “Prompted Playlists” in beta. I built just a few playlists and discovered that the LLM behind the agent tries to meet your request, but fails since it doesn’t know enough but...

Rules fail on the prompt, succeed on the boundary

Prompt injection is persuasion, not a bug Security communities have been warning about this for several years. Multiple OWASP Top 10 reports put prompt injection, or more recently Agent Goal Hijack, at...

TDS Newsletter: Beyond Prompt Engineering: The Latest Frontiers of LLM Optimization

Never miss a brand new edition of , our weekly newsletter featuring a top-notch number of editors’ picks, deep dives, community news, and more. Most of the issues practitioners encountered when LLMs first burst onto the...

Why the Sophistication of Your Prompt Correlates Almost Perfectly with the Sophistication of the Response, as Research by Anthropic Found

, the thought has circulated within the AI field that prompt engineering is dead, or not less than obsolete. This, on one side because pure language models have turn out to be more flexible...

Automatic Prompt Optimization for Multimodal Vision Agents: A Self-Driving Automobile Example

Optimizing Multimodal Agents Multimodal AI agents, those who can process text and pictures (or other media), are rapidly entering real-world domains like autonomous driving, healthcare, and robotics. In these settings, we now have traditionally used...

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