Tag Archives: Prompt engineering

23Mar/26

Poisoned Memories and Fake News: The Vulnerable Intersection of AI and Algorithmic Trading

March 23, 2026Security1, AI agent, AI safety, Artificial intelligence, Code injection, Deep learning, Exploit, Intelligent agent, Large language model, Natural language processing, Ninad Pathak, OpenAI, Prompt engineering, Prompt injection, Vulnerabilityadmin

March 23, 2026 /Mpelembe Media/ — The provided sources comprehensively detail the rapid evolution of artificial intelligence from conversational large language models (LLMs) to autonomous “agentic AI,” and the massive security challenges accompanying this shift. As AI agents gain the ability to retrieve information, orchestrate multi-step workflows, and execute high-privilege actions (like trading or system administration), they introduce unprecedented attack surfaces across enterprises and Web3 ecosystems.

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12Mar/26

The Death of the Résumé in the AI Era

March 12, 2026BusinessAI agent, AI boom, Artificial general intelligence, Artificial intelligence, automattic, ChatGPT, Computational neuroscience, Deep learning, Douglas Guilbeault, expensify, Generative artificial intelligence, GitHub, Jim Farley, LINKEDIN, Machine learning, Prompt engineering, Recruitment, Résumé, Ronald Coase, United Statesadmin

The Resume Is Dead (And Other Counter-Intuitive Truths About the 2026 Job Market)

March 10, 2026 /Mpelembe Media/ — The traditional employment résumé is becoming increasingly obsolete as generative AI allows job seekers to flood the market with indistinguishable, buzzword-heavy applications. Because digital tools can now easily fabricate credentials and cover letters, hiring managers are frequently ignoring these documents in favor of more authentic evaluation methods. Many companies are shifting toward skills-based hiring, which prioritizes practical assessments and paid work trials over prestigious degrees or past job titles. Recruiters find that a candidate’s actual real-time abilities are far better predictors of success than a polished list of achievements that may have been written by a bot. Consequently, the modern job market is demanding more tangible proof of talent, as traditional paper applications fail to distinguish high-quality candidates from automated noise. Continue reading →

25Feb/26

Stop Guessing Your Prompts: 4 Game-Changing Lessons from the Vertex AI Prompt Optimizer

February 25, 2026TechnologyArtificial intelligence, Context engineering, Deep learning, Gemini, Generative artificial intelligence, Google DeepMind, Large language models, Machine learning, Natural language processing, Program optimization, Prompt engineering, Prompt injection, Veoadmin

Maximizing AI Accuracy: Automating Workflows with the Vertex AI Prompt Optimizer

23 Feb. 2026 /Mpelembe Media/ — The Vertex AI Prompt Optimizer is a tool designed to refine AI instructions automatically using ground truth data. By comparing initial outputs against high-quality examples, the system iteratively adjusts system prompts to achieve greater accuracy and consistency. The author illustrates this process through a Firebase case study, where the tool was used to transform rough video scripts into professional YouTube descriptions. Although the optimization process requires an upfront investment in time and tokens, it significantly reduces the need for manual human intervention. Ultimately, the source highlights how data-driven optimization can replace trial-and-error prompting with a more reliable, automated workflow. Continue reading →

25Nov/25

The value of thought. How human-AI collaboration is measured economically

November 25, 2025Economy.google, AI alignment, Artificial general intelligence, Artificial intelligence, Computational neuroscience, Cybernetics, Deep learning, Large language model, Natural language processing, OpenAI, Prompt engineering, Tokenizationadmin

This touches on how large language models (LLMs) operate! tokenization is the fundamental process in natural language processing (NLP) of breaking down raw text into smaller units called tokens, such as words, subwords, or characters. This is a crucial first step that transforms unstructured text into a structured format that machine learning models can process.

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01Jan/25

NotebookLM: Features and Use Cases

January 1, 2025Innovation.google, AI software, Alphabet Inc., Artificial intelligence, Automatic summarization, Computational linguistics, Computational neuroscience, Deep learning, Google Search, Multi-document summarization, Natural language processing, NotebookLM, Prompt engineering, Question answering, Romeadmin

Jan. 01, 2024 /Mpelembe Media/ — NotebookLM is an experimental tool by Google that helps you understand and work with information from your notes and documents. It uses AI to summarize, answer questions, and generate new insights from your content. Continue reading →

29Apr/23

The art of crafting and building keywords in an “AI Prompt” effectively

April 29, 2023InternetAlan Turing, Deep learning, Generative artificial intelligence, Index term, Linguistics, Natural language processing, Prompt engineering, Reserved word, Response Prompting Procedures, United States, Unsupervised learningadmin

Generating high-quality content for businesses, websites, and other uses is critical. However, creating engaging and effective content is a time-consuming and challenging task to accomplish. The use of keywords in Artificial Intelligence prompting (AI Prompting) has the potential to improve productivity and creativity by assisting in the content authoring or generation process.
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