prompt engineering
PulseAugur coverage of prompt engineering — every cluster mentioning prompt engineering across labs, papers, and developer communities, ranked by signal.
- instance of Harness Engineering 70%
- competes with Loop engineering of amadoriase II and mutational cooperativity 70%
- instance of Royal Galician Academy 70%
- instance of Loop engineering of amadoriase II and mutational cooperativity 70%
- other Context Engineering 60%
- other fine-tuning 60%
- affiliated with Vector Databases 60%
- affiliated with Harness Engineering 50%
- other Vector Databases 50%
- affiliated with Loop engineering of amadoriase II and mutational cooperativity 50%
- other Towards AI 50%
- other Loop engineering of amadoriase II and mutational cooperativity 50%
22 day(s) with sentiment data
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Context Engineering: The New Frontier Beyond Prompt Engineering
Context Engineering is emerging as a critical discipline in AI, moving beyond prompt engineering to focus on designing and managing the information an AI system receives. This approach ensures AI models have access to r…
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Semantic Layer, Not Text-to-SQL, is the Real LLM Interface
The article argues that the focus on Text-to-SQL for LLMs has been misguided, as the true challenge lies in understanding business logic, not just SQL syntax. Business definitions, such as what constitutes 'active users…
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Structural Harness method boosts small LLM performance
A new approach called "Structural Harness" is proposed for improving the performance of smaller language models, specifically those with 3B to 8B parameters. This method involves separating the core cognitive mechanics …
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Model Context Protocol (MCP) shifts AI agent interaction from APIs to self-describing tools
The Model Context Protocol (MCP) is a new approach that changes how AI models interact with APIs. Unlike traditional APIs, which require developers to explicitly instruct models on how to use each tool, MCP allows tools…
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AI 'engineering' terms like loop and graph engineering spark debate
The terms "loop engineering" and "graph engineering" have recently gained traction in AI discussions, largely due to viral social media posts. These terms, however, are seen by some as evolving or renaming of existing c…
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New system AutoJourn tackles bias in AI-generated news
Researchers have developed AutoJourn, a system designed to enhance the responsible creation and evaluation of news generated by large language models (LLMs). The system addresses challenges in automated journalism by ex…
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Smart glasses and LLMs to aid visually impaired individuals · 3 sources tracked
Researchers are exploring how to use smart glasses and large language models (LLMs) to assist visually impaired individuals. The goal is to develop user-centric prompt engineering techniques that can be integrated into …
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Prompt engineering debate: LLMs need structured input, not just natural language
The debate around prompt engineering for Large Language Models (LLMs) continues, with some arguing it's an outdated concept as AI evolves to understand natural language more effectively. Others contend that LLMs still r…
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AI System Design Surpasses Prompt Engineering for Production Use
Prompt engineering, while still relevant, is becoming insufficient as AI systems move from experimentation to production. The focus is shifting towards AI system design, which encompasses the entire environment around t…
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Observability for Agentic Swarms: Managing Multi-Agent AI Complexity
The article discusses the shift from single-agent LLM applications to complex multi-agent systems, known as Agentic Swarms. It highlights the critical need for Multi-Agent Orchestration Observability to manage the compl…
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WeAreDevelopers World Congress 2026 set for Berlin with AI focus
The WeAreDevelopers World Congress 2026 is scheduled to take place in Berlin, with Day 0 dedicated to a hands-on workshop on prompt engineering. This event will feature sessions on trusting code, the official opening of…
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Prompt Engineering Evolves with AI Agents, Future Reliance Debated
The concept of prompt engineering is being redefined in the era of AI agents, shifting focus from simple command input to more complex, strategic interactions. One perspective explores how reliance on AI might lead to r…
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LLM, MCP, and RAG field guide targets AI engineers
This item is a comprehensive field guide for engineers focused on Large Language Models (LLMs), the Model Context Protocol (MCP), and Retrieval-Augmented Generation (RAG). It is designed for professionals in AI engineer…
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Eval-driven development prioritizes measurable wins over subjective prompt tuning
Eval-driven development is presented as a superior method to prompt engineering for improving AI models. The core principle is to first establish a failing evaluation metric, then iteratively refine prompts to meet that…
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New agentic tutor 'Prompt Coach' improves developer prompt engineering skills
A new paper introduces Prompt Coach, an agentic tutor designed to help software developers learn prompt engineering skills. This tool provides Socratic guidance within an IDE, evaluating prompt quality and offering targ…
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AI Workflow Frameworks: Prompt-based, LangGraph, Temporal, and n8n Compared
The article compares four AI workflow frameworks: Prompt-based, LangGraph, Temporal, and n8n, highlighting their distinct approaches to workflow definition, state persistence, and execution engines. Prompt-based workflo…
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Enterprise LLM Engineering Guide Focuses on System Reliability and Security
This guide focuses on enterprise LLM engineering, emphasizing the creation of reliable, observable, and secure systems around large language models rather than just prompt engineering. It details core components, archit…
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AI development shifts focus from prompt engineering to context engineering, raising security questions
The concept of "context engineering" is emerging as a potential successor to traditional prompt engineering in AI development. This approach involves more deeply integrating context into AI systems, moving beyond simple…
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Prompt Engineering, RAG, and Fine-Tuning: Choosing the Right LLM Tool
This article explores the distinctions and appropriate uses of prompt engineering, retrieval-augmented generation (RAG), and fine-tuning in the context of large language models. It emphasizes the importance of diagnosin…
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Fine-tuning LLMs explained with human social learning analogy
Fine-tuning in large language models can be understood through a human analogy of learning social behavior. Prompt engineering is akin to temporary instructions given before an event, while instruction tuning involves t…