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Agents read 83.6% of the relevant code on turn 1 and 35.4% by turn 5. Check the transcript for a search before the write, then gate the diff with jscpd.
Does being rude to AI improve answers or lower costs? We tested six models to separate prompt tone from brevity instructions and examine what changes. The post Does being rude to AI work? What 6 models revealed appeared first on Arize AI.
As AI coding agents improve, intent engineering, not prompt tricks, is becoming the skill that determines whether their output is actually useful.
In a 30-condition eval, telling Codex to use TDD cut fully correct Zstd decoders by 5 to 7 points. No testing instruction clearly beat none.
Everyone claims to know how to prompt. Almost nobody knows how to audit, govern, or chain models in production. What Anthropic’s CCAO-F…Continue reading on Towards AI »
Pratik Rasam discusses how Spotify Ads Manager runs production-grade multi-agent systems at scale using Google ADK Java. He shares key architectural patterns, domain ownership models, deterministic guardrails, and tracing-based evaluation strategies, detailing hard-learned lessons on drawing agent…
Why AI Safety Needs Register Robustness The Language Problem AI Evaluation Is Missing An AI assistant should adapt to how people speak without changing its standards for accuracy, uncertainty, or …
Claude Code Caveman Eval: Two Sentences Took the Median Cut From 14.6% to 50.6% I recounted the repo’s own eval snapshots: the jump came from two added sentences, v3.1.0 still ships the core …
10 image to prompt generators compared by what they output: Midjourney prompts, structured JSON, CLIP tags, or plain description. Free tiers verified for 2026.
Opus 5.5 sends its between-tool notes as empty thinking blocks and ends some turns early. What breaks in an agent harness, and the documented fixes.
Large language models can answer questions, summarise documents, write code, and interact with external systems. But building a reliable AI application requires more than sending a prompt and displayi
A mandated clarifying-question rule fired on 0 of 4 arms. Removing the broader rule it lost to restored it on 4 of 4. Why structure beats instruction, measured.
Assign each reference image a role, set priorities when the images conflict, and check the subject, composition, and style in the generated result.
Nine dated reference-edit outputs, exact prompts, and failure checks. Learn when background replacement needs source-layer compositing to preserve the person.
Two frontier labs and two research papers reached the same conclusion by 2026: the AI instructions you wrote to improve output are now quietly making it worse.
Read a prompt by separating its subject, composition, lighting, style, and constraints. Learn what an example can show and what still needs testing.
Inspect lighting, anatomy, materials, and composition in AI images, then test targeted changes to the prompt or editing workflow.
Use lens, aperture, viewpoint, and depth-of-field language to describe a visual goal, while separating photographic concepts from actual model controls.
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