Inkling: Mira Murati's Conversational AI Model
Inkling is Mira Murati's conversational AI model designed to engage users in natural, human-like dialogue while demonstrating advanced language understanding
Explore all tips and tricks tagged with "prompting".
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Inkling is Mira Murati's conversational AI model designed to engage users in natural, human-like dialogue while demonstrating advanced language understanding
Researchers demonstrate how adversarial physical patches placed in real-world environments can manipulate vision-language models to generate harmful or
Graph engineering transforms raw data into structured networks that enable advanced analytics, pattern recognition, and AI applications far beyond simple
SGLang demonstrates superior performance compared to Hugging Face Text Generation Inference in recent benchmark tests, showing faster processing speeds and
Exploring how combining multiple affordable AI models can outperform a single premium model through ensemble techniques, offering cost-effective solutions for
Moonshot AI introduces Kimi K3, a groundbreaking long-context language model that processes extended documents and conversations with unprecedented efficiency
Microsoft 365 Copilot introduces an AI learning agent that helps users understand and master productivity features through personalized guidance and
Prompt caching reduces LLM API costs by up to 90% by storing and reusing repeated context across multiple requests, eliminating redundant processing and
AI agents automatically categorize, prioritize, and route customer support tickets to appropriate teams, reducing response times and improving service
This guide explores using large language models as automated judges to evaluate AI model outputs on Azure, covering implementation patterns, best practices,
Organizations can enhance AI reliability by integrating systematic quality control measures throughout their AI workflows, including validation checkpoints,
Anthropic unveils Claude Fable 5 with a dual-model strategy featuring both a high-performance flagship and an efficient lightweight version for diverse AI
Claude Fable 5 launches with enhanced Auto Opus 4.8 routing capabilities, offering improved performance and intelligent request handling for more efficient AI
AI language models produce varying responses to identical prompts due to temperature settings, model updates, and inherent randomness, creating challenges for
This guide explains how to run 70-billion parameter language models on local hardware, covering system requirements, optimization techniques, and practical
This guide explores three key techniques for grounding large language models—Retrieval-Augmented Generation, fine-tuning, and prompt engineering—to improve
Subword tokenization explains why language models read words as chunks rather than letters, making character-level tasks awkward for them.
LayerDiffuse lets diffusion models produce native transparent images and separate layers using a latent transparency technique.
The self-consistency method samples several reasoning paths from a language model and picks the most common answer, raising accuracy on reasoning tasks.
Qwen's Qwen2-Audio model accepts audio input and responds with text, supporting voice chat and audio analysis through the transformers library.
Anthropic's prompt engineering guidance shows how a system prompt sets a model's role, tone, and behavior through clear, specific instructions.
Structured JSON output lets AI models feed downstream systems directly through schema-enforced function calls instead of simulating user interactions.
OpenAI's prompt engineering guide highlights vague instructions, missing context, and undivided complex tasks as habits that weaken GPT results.
Google DeepMind's OPRO method had an AI generate the prompt 'take a deep breath,' which scored 80.2 on GSM8K math problems.
A research paper introduces self-debugging, a method that teaches language models to find and fix their own code errors without human feedback.
How the temperature parameter in the Claude API shapes creative output, and why writers move it closer to 1.0 for generative work.