In episode 2 of Generationship, Rachel Chalmers speaks with Liz Fong-Jones and Phillip Carter of Honeycomb. Together they explore use cases for LLMs, insights on navigating AI hallucinations, the tradeoffs between fine-tuning and prompt engineering, and the privacy and security implications inherent to commercial LLMs.

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Generationship - Ep. #2, Putting LLMs to Work with Liz Fong-Jones and Phillip Carter of Honeycomb

Middle East AI News Minute - brought to you by Carrington Malin - Marketing for an AI First World. In this podcast: Dubai's global prompt championship to take place next week; Saudi Arabia launches digital ID for pilgrims; ADIA Lab signs MoU with Indra company, Minsait.

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Middle East AI News Minute - 17-May-24

Good morning, everyone! It’s September Ninth, Two Thousand Twenty-Four, and we're here with your latest AI Tech News Today. Let’s dive into the highlights.

anthropic experts share top tips for effective AI prompting. Anthropic has hosted a roundtable discussion to share strategies for effective prompts. Their experts emphasize the need for clarity and specificity, likening prompts to precise GPS coordinates. In business contexts, including examples can help models understand expectations. However, in research, less is often more. They recommend avoiding overloading prompts with examples to retain creative flexibility. Continuous adjustments and iterative testing are key. The team predicts that as models advance, prompt engineering will shift to a more collaborative role.

openAI says "GPT Next" timelines shared by employees are just placeholders. OpenAI has clarified that the much-anticipated timelines for “GPT Next” are more aspirational than concrete commitments. High-profile projections claimed that this new model would outperform GPT-4 by a hundredfold. While expectations are high, these timelines should be taken with a grain of salt as they sketch out potential advancements.

openAI's Sora is stuck in research limbo as the company courts Hollywood and policymakers. OpenAI’s video AI model, Sora, has no firm release date yet due to ongoing discussions with policymakers and the entertainment industry. There are concerns related to security, especially in light of upcoming United States elections. Additionally, unresolved technical issues and high generation costs have hampered its launch. Meanwhile, OpenAI is actively engaging with Hollywood to showcase Sora’s potential for creative projects.

If you've been keeping up with AI developments, you’ve likely spotted new startups and tools popping up all over the tech landscape. This week alone, five new AI-powered tools have emerged, making tasks easier and more efficient for businesses and individuals alike. 

That’s your AI news for today! Keep following us to stay updated about the fascinating advancements and trends in the world of A-I. Have a fantastic day!

Anthropic Offers Insights on Effective AI Prompting as OpenAI Clarifies GPT Next Timelines and Sora's Release Challenges

This is a podcast based on a selection of scientific literature regarding prompt engineering.
Generated by Google NotebookLM

Prompt Engineering

Prompt Engineering for Product Managers

Discover how Generative AI creates unique content and how RAG uses real-time data for precision. Explore essential tips, tools, and steps now.

how generative ai works

Prompt engineering creates succinct commands to direct and guide artificial intelligence (AI) tools. Here, we explore how to develop your prompt engineering skills to get optimal output from AI models.

Prompt engineering interacting with generative AI tools

Wie bekomme ich die richtigen Antworten auf meine Fragen? Wie bringe ich KI dazu mir so manch unliebsame Arbeit abzunehmen? Das weiß KI-Expertin Mingyang Ma, Principal AI Strategist bei Applied AI. Zusammen finden wir im Podcast heraus, was Prompt Engineering ist und welche Aufgaben zu ihrem Alltag als Prompt-Spezialistin gehören. Sie erklärt uns was es zu beachten gilt und wie wir Fehler vermeiden können.

#47 Mit Klarheit & Präzision zur richtigen Antwort: Prompt Engineering / Gast: Mingyang Ma

Registration for Global Prompt Championship open

Prompt Engineering Guide

A real-world walkthrough of the Brain Garden system — how structured rules, task lists, skill jacks, and human-directed prompts create smarter AI-assisted development workflows. No hype, just craft. This track accompanies the full Medium article "Building with Brain Garden: Real-World Lessons in Practical AI." Learn how AI can supercharge engineering when used systematically and responsibly.

Building with Brain Garden: Real-World Lessons in Practical AI

Welcome back to **AI with Shaily**! 🎙️ I’m Shailendra Kumar, your friendly guide through the fascinating and sometimes quirky universe of artificial intelligence 🤖✨. Today’s episode dives deep into a tricky AI challenge: hallucinations. Now, before you imagine your AI seeing unicorns or dancing robots 🦄🤖💃, let me clarify—we’re talking about those moments when AI confidently makes up information that’s completely false or fabricated. This is a big deal, especially as AI becomes more integrated into critical fields like healthcare and law.

Here’s the big question: how do we tell fact from fiction when even the smartest digital assistants sometimes spin tall tales? This issue is buzzing everywhere as of May 2025, from social media to innovation hubs, and it’s not just a nerdy puzzle—it’s vital for trustworthy AI systems.

I want to share a quick story to highlight the stakes. While advising a healthcare startup, I saw their AI confidently cite a medical study that didn’t exist. 😳 That kind of hallucination could literally cost lives! It reminded me how crucial it is to have multiple layers of checks and balances to keep AI grounded in reality.

So, what are the top strategies AI pros use to evaluate and manage hallucinations? Here are five best practices:

1. **Retrieval-Augmented Generation (RAG)** 📚🔍  
Think of RAG as a trusty fact-checker riding shotgun with your AI. Instead of relying solely on old training data, RAG pulls in real-time, authoritative info from external sources to verify facts before answering. It’s not a perfect cure, but it’s a huge step forward in reducing hallucinations.

2. **Clear, Precise Prompts** ✍️🎯  
Your words matter! Vague or ambiguous prompts are like giving your AI a foggy map—leading to confusion and invented answers. Precise, well-crafted prompts help guide the AI to accurate outputs and keep hallucinations at bay. It’s the “measure twice, cut once” rule for AI prompting.

3. **Active Hallucination Detection** 🔎🤖  
Tools like SelfCheckGPT compare multiple AI-generated answers side-by-side to spot inconsistencies that signal hallucinations. Pairing this with external validation—cross-checking outputs against trusted databases like medical journals or legal archives—can achieve up to 94% accuracy in spotting errors. That’s impressive when the stakes are high!

4. **Human Feedback and Custom Guardrails** 👩‍⚕️🛡️  
No AI system is perfect without human oversight. Experts review AI outputs and establish domain-specific rules—guardrails—that prevent nonsensical or faulty responses from slipping through. Think of these guardrails as polite but firm chaperones keeping AI behavior in check.

5. **Automated Prompt-Based Hallucination Detectors** 🤖⚡  
These smart helpers use targeted AI prompts to double-check facts on the fly, enabling organizations to scale quality checks efficiently and cost-effectively.

And here’s a **bonus tip** for AI developers: keep your training data fresh and rigorously verified. Outdated or biased datasets are major causes of hallucinations. Regularly updating your model’s knowledge is like giving your AI a daily news briefing—keeping it sharp and current.

To sum it up: tackling AI hallucinations requires a multi-layered approach—smart prevention, sharp detection, solid validation, and a human safety net. Think of it as building a fortress against fake facts—strong, adaptable, and layered.

I’ll leave you with a quote I often reflect on when working with AI:  
*"Truth exists in layers—our job is to peel them back carefully, with both machines and humans working hand in hand."* 🤝💡

Thanks for tuning into **AI with Shaily**! Don’t forget to join the conversation—follow me on YouTube, Twitter, LinkedIn, and Medium. Subscribe for more AI insights and share your thoughts in the comments. After all, the best AI is built not in isolation but in community. 🌐💬

Until next time, keep questioning and stay curious! 🔍✨

AI Hallucinations Exposed: How to Spot and Stop Your Digital Assistant’s Tall Tales

In our final episode of the series, we dive deep into the fascinating world of Prompt Engineering — the new superpower every AI-driven professional needs. 🤖💬
 
From understanding AI’s language to designing powerful prompts that unlock the full potential of tools like ChatGPT, this episode is your gateway to mastering communication with AI.
 
✅ What is Prompt Engineering?
✅ Why is it becoming an essential skill?
✅ How can you start building smarter prompts today?
 
Whether you're in tech, marketing, HR, or education — prompt engineering is the skill that’ll set you apart in the AI-first era.
 
🎓 Join us for AI+ Prompt Engineer Level 1 and start talking to AI like a pro.
https://www.aicerts.ai/certifications/essentials/ai-prompt-engineer

AI+ Prompt Engineer Level 1 — Talking to AI_ The Future of Prompt Engineering

Die vorliegenden Quellen bieten einen umfassenden Überblick über das Prompt Engineering und Techniken zur Verbesserung der Interaktion mit künstlicher Intelligenz. Sie betonen die Wichtigkeit klarer, detaillierter und kontextbezogener Anweisungen für optimale Ergebnisse, wie das Hinzufügen von Beispielen oder die Nutzung von Persona-Definitionen. Mehrere Beiträge befassen sich mit fortgeschrittenen Methoden wie "Chain-of-Thought"-Prompting, "Few-Shot"-Prompting und multimodalen Inputs. Die Quellen diskutieren auch die Praxis der Iteration und des Experimentierens mit verschiedenen Formulierungen, um die KI-Ausgabe zu verfeinern, und beleuchten dabei häufige Fehler wie das Fehlen wichtiger Hintergrundinformationen oder die Gefahr von Halluzinationen der KI.

Welcome back to *AI with Shaily,* hosted by Shailendra Kumar, a lively and insightful show that brings you the latest and greatest in artificial intelligence with a personal touch! 🎙️🤖 Today’s episode dives deep into how Large Language Models (LLMs), like GPT, are revolutionizing traditional machine learning workflows in exciting new ways.

Shailendra uses a tasty cooking analogy 🍳 to explain the transformation: imagine you’ve always prepared every ingredient yourself, but now you have a smart kitchen assistant who not only helps prep but suggests new spices you never considered. That’s exactly what LLMs are doing for feature engineering — automating and enhancing the process of extracting meaningful features from complex data like text and images. This upgrade is like swapping a butter knife for a chef’s knife — sharper, more efficient, and precise. 🔪✨

The episode also highlights a hot topic buzzing on LinkedIn and Twitter: the merging of MLOps (machine learning operations) and LLMOps (operations focused on large language models). Teams are creating hybrid systems where traditional ML algorithms and LLMs coexist, deployed and monitored seamlessly. Frameworks like LangChain are gaining popularity, helping move projects from prototype to production at lightning speed. ⚙️🚀 Shailendra shares his excitement about this convergence, comparing it to his first experience automating an ML deployment — only now on a much grander scale!

Another fascinating point is how LLMs are not just analyzing data but actively augmenting the training process itself. They can generate synthetic data, provide feedback, and identify the most important samples to improve dataset quality — acting like a coach who trains and scouts the best plays on the field. 🏈📊 Plus, hybrid inference pipelines are emerging, where LLMs handle unstructured text with their natural language skills, while traditional ML methods process structured data. This combo is especially powerful in sensitive fields like healthcare and finance, where both numerical accuracy and subtle context matter deeply. 💉💰

Importantly, Shailendra emphasizes the growing trend of bringing humans back into the AI loop. This blend of AI power and human judgment ensures trustworthy, explainable outcomes. Imagine an LLM flagging a tricky prediction for a human expert’s review or generating simple explanations so users aren’t lost in technical jargon. It’s a perfect balance of machine efficiency and human insight — a crucial step toward responsible AI. 🤝🧠

Before signing off, Shailendra shares a valuable pro tip: invest time in prompt engineering tailored to your specific datasets. Fine-tuning the questions you ask LLMs can dramatically boost feature quality and overall model performance. It’s like the secret seasoning that turns a good dish into a wow-worthy meal! 🌶️✨

He leaves listeners with a thought-provoking question: How ready are you to blend these powerful language models into your existing ML setups? Are you prepared to let AI be your sous-chef in the data kitchen? 🍽️🤔

To inspire, Shailendra quotes AI pioneer Andrew Ng: “AI is the new electricity,” reminding us that this powerful force is now being wired directly into the data pipelines fueling our systems. ⚡🔌

Don’t miss out on more episodes of *AI with Shaily* available on YouTube, Twitter, LinkedIn, and Medium. Subscribe for your weekly dose of AI breakthroughs and share your thoughts to keep the conversation vibrant and exciting! 🌐📲

Till next time, keep experimenting, questioning, and AI-ing smartly! This is Shailendra Kumar signing off from *AI with Shaily.* 🎧🤓✨

LLMs as Your AI Sous-Chef: Revolutionizing Machine Learning Workflows with Hybrid Intelligence

Welcome to *AI with Shaily*! 🎙️ Hosted by Shailendra Kumar, a passionate male AI enthusiast, this engaging podcast dives deep into the fascinating world of artificial intelligence and prompt engineering. Shailendra, affectionately known as Shaily, guides listeners through the evolving landscape of AI, making complex topics accessible and exciting.

In this episode, Shaily shares a compelling story about the art of prompt engineering—how crafting the perfect prompt is like being a DJ mixing tracks to create the ideal vibe 🎧. However, as AI models become more advanced, the role is transforming into what he calls “AI choreography” 💃🤖. Instead of simply nudging AI with a prompt, it’s about orchestrating a symphony of AI responses, managing multiple models to achieve harmonious results.

Shaily highlights the intriguing variability among AI models, comparing them to twins who respond differently to the same question 👯‍♂️. This unpredictability keeps prompt engineering relevant and dynamic, emphasizing that success depends not only on the question asked but also on understanding which AI model you’re interacting with and how it interprets your prompt.

He also points out the growing popularity of prompt engineering bootcamps and courses 📚, where learners master skills like fine-tuning text generation with ChatGPT and creating stunning visuals using tools like DALL-E and Stable Diffusion 🎨. These educational programs demonstrate that despite technological advances, knowing the strengths and quirks of AI remains a valuable skill.

On the social media front, Shaily notes how AI influencers and smarter algorithms are reshaping platforms like Pinterest and Instagram 📱✨, blending human creativity with AI capabilities to revolutionize digital storytelling and communication.

As a practical takeaway, Shaily advises experimenting with prompts across different AI models to gain diverse perspectives and richer insights 🔄💡. This approach is akin to consulting multiple experts before making a decision.

Ending with a thoughtful question, Shaily invites listeners to ponder whether humans will become expert choreographers directing AI, or if AI will take the lead in this evolving dance 🕺🤖. He closes with an inspiring quote from Alan Turing: *“We can only see a short distance ahead, but we can see plenty there that needs to be done.”* — a perfect reflection of the exciting journey ahead in prompt engineering.

Listeners are encouraged to connect with Shailendra Kumar on YouTube, Twitter, LinkedIn, and Medium under *AI with Shaily* to stay updated, share ideas, and be part of this vibrant AI community 🌐🤝.

In summary, *AI with Shaily* is a lively, insightful podcast hosted by Shailendra Kumar, exploring the nuanced world of AI and prompt engineering with enthusiasm, creativity, and a forward-looking spirit. Stay curious and creative! 🚀✨

AI with Shaily: The Future of Prompt Engineering and AI Choreography

TL;DR - Unlocking AI: The Quick Guide

Mastering AI means going beyond basic commands to strategically engineer prompts and optimize Large Language Models (LLMs). This deep dive into prompt engineering is critical for efficient, accurate, and cost-effective AI solutions.

You'll learn to craft precise instructions using key elements and leverage advanced techniques like Few-Shot, Chain of Thought, and Retrieval Augmented Generation (RAG)—the latter being vital for cost control and accuracy. Fine-tuning LLM outputs with settings like Temperature and Max Length is also key.

Successful enterprise AI adoption relies on a layered framework, from data quality to strategic integration and the emergence of collaborative Generative AI Networks (GAINs). While AI offers immense business impact, challenges like cost and ethics demand careful consideration for true transformation.

07th July 2025, Unlocking AI's Full Potential: The Power of Prompt Engineering and LLM Optimisation

We discuss Prompt Declaration Language (PDL), a revolutionary approach to AI infrastructure that addresses the current "black-box" nature of prompt engineering. It argues that traditional methods, relying on unpredictable "magic prompts", are unsustainable and lead to a scalability crisis in enterprise AI. PDL, developed by IBM and open-sourced, utilizes a transparent, YAML-based declarative language to define AI interactions and workflows, transforming opaque prompts into version-controlled, debuggable, and optimizable components. This shift promises to make AI development more akin to traditional software engineering, enabling collaboration, reliability, and improved performance through explicit, reviewable, and testable AI logic. The text emphasizes that embracing such transparent and accountable AI practices is crucial for future innovation and trustworthiness in AI systems.

Bit & Being - PDL is to Prompts as git was to Code

🎧 NEW TRACK DROP: CLAUSE 808
This ain’t a diss. It’s a deposition.

Dedicated to the software companies who:

Used their competitor’s AI outputs in marketing

Got called out by their own users

Exploited my unpaid labor

Reverse-engineered my Canva pitch deck

Silenced my voice while copying my syntax

You didn’t build culture. You mined it.

I’m the founder, the teacher, the tester, the trap priestess.
And I’ve got prompts and receipts.
Clause 808 is for every visionary who built without credit, got gaslit, and still shipped better than your dev team.

Listen. Share. Cite the source.
Empress out.

#Clause808 #TrapBruja #TechHop #PromptQueen #BuildPortalsNotPlugins #AIWithSoul #IPTheftAnthem #LegaleseRap #EmpressEra #SacredTrap #UndergroundLegend #SingleMomCEO #SpanglishBars #EditorialRealism #RitualRap #SoftwareDissTrack #CodingInCandles #WitchingHourWorkflow #UnpaidLaborAnthem #BrokeButBuilding #SunoRap #EthicalPrompting #BrujaBars #Glitchtrap

Clause 808

The Middle East AI News Minute gives you a daily one-minute daily briefing on the top AI-related stories coming out of the Middle East. If you are a busy technology, business or government leader who want to get a quick update, then this is the podcast for you.

Heavybit is the leading early-stage investor in enterprise infrastructure. Since 2013, we've helped launch and scale visionary technical startups—from DevSecOps and feature flagging to AI code generation and beyond. We go hands-on from day 0, backing founders as they turn code into companies and build the future of software from the bottom up to the top down.

Unlock the secrets of data mastery with Shailendra, a 27-year veteran in AI, ML, and Data Science, driving AU$ 6 Billion in transformative value. As the author of 'Making Money out of Data,' their strategies redefine digital success. With expertise honed at IBM and Accenture, they drive innovation and data monetization. Explore how loyalty programs like Flybuys and Everyday Rewards revolutionize business—connect with Shaily today.

Composer, Sound Designer, Producer, Drumset Artist and Tabla Performer...The musical mind of a passionate, musician, entrepreneur, idealist, thinker, philosopher, truth seeking, romantic.


This is my own personal playlist for myself. If you enjoy the music too, excellent. You are welcome to stay and talk Spellwork, Tech, Corporate Corruption and Betrayals with me. If it's not your thing? Please show yourself to the door. I don't make music for you. I make music for me. This isn't even monetized. I do this for free. For me. For Spirit. For My Altar. For what is right and in full alignment. Not your approval or entertainment.

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Building a better future through technology

Hi I am Aygün. I glue AI with smart spaces.
I have completed my credits here and migrated to spotify.
https://open.spotify.com/show/5iNiriwBv8WnBQPGFh6B9F?si=AeQhTeZLTpygNwEW_tkQug

Die Route ist berechnet: Ziel 2030. Die Koffer vollgepackt mit Fragen über die Entwicklungen der Technologie in dieser Dekade und deren Auswirkungen auf Wirtschaft, Gesellschaft, Bildung, Gesundheitswesen und Branchen. In der ersten deutschsprachigen Dell Technologies Podcast-Reihe „Road to 2030“ starten wir ab sofort den Technologie-Dialog mit Branchenexperten, Forschern und spannenden Persönlichkeiten.

ICAEW is a professional membership organisation that provides insight and leadership to the global accountancy and finance profession.

New tracks tagged #Prompt Engineering