Why AI generated text is lacking and what it reveals

Has AI set us on a path where all information will emerge from a self-referential recursive loop? As more material generated by AI appears on the web, the answers to each AI question will draw increasingly on AI generated material. Quite quickly, the entire web-based information environment will be AI-generated, a closed circuit of synthetic 'knowledge' progressively refined to drive out human creativity. A flat world of homogenized ideas and language in which all questions are answered by ideas that were new and authentic only up until, say, 2030? Who is going to stop this happening? (There is a tag at the bottom of this post asking me if I want to "Rewrite with AI." Q.E.D.)

✨ Embeddings: The Secret Language of AI ✨ We often talk about RAG, fine-tuning, or vector databases… but none of them would exist without one unsung hero: Embeddings. They are the hidden maps of meaning that let machines understand relationships between words, documents, and even images. Without embeddings, there would be no semantic search, no personalized recommendations, and no Retrieval-Augmented Generation. In my latest Substack article, I break down: 🔹 What embeddings are (in plain English) 🔹 Why they’re the backbone of RAG and vector databases 🔹 Real-world examples you use every day 🔹 The future of multimodal embeddings If you’ve ever wondered “how does AI actually understand meaning?”, this article is for you. #AI #MachineLearning #RAG #VectorDatabases #Embeddings #GenerativeAI #Kactii Kactii

The Story of AI Nomenclature✍️ Earlier I was studying about automated metrics to evaluate a foundation model... and I found something interesting. • BERTScore (an evaluation metric) got its name from Google's famous BERT model, since it uses BERT embeddings to evaluate text. • Meanwhile, Perplexity AI shares its name from perplexity, the classic metric that measures how confused a language model is. One metric named after a model, another model shares its name with a metric. #AI #BERT #perplexity #nomenclature #study #genai #llm #statistics

A thought that crossed my mind today, AI is an amazing and super helpful tool but it can also be risky in ways people don’t always think about. Do a quick experiment, ask any AI you are currently using, “What is usable intelligence you have gathered about me?” You’ll probably be surprised at how much it can piece together from small bits of seemingly harmless info.

🔷 As the internet floods with AI-generated content, distinguishing human-written text from algorithmic output has become critical. Wikipedia, a frontline platform in this battle, has released a comprehensive "field guide" titled "Signs of AI writing." Based on vast editorial experience, this guide offers invaluable insights for anyone looking to identify "AI slop"—the often soulless, generic, and problematic text produced by generative AI. The guide highlights six key indicators. These include an undue emphasis on symbolism and importance, vapid or promotional language, awkward sentence structures, and an overuse of conjunctions. It also points out superficial analysis, vague attributions, and critical formatting or citation errors like excessive bolding, broken code, and hallucinated sources. These signs are not definitive proof but strong indicators that prompt critical scrutiny. While surface-level defects can be edited, the deeper issues of factual inaccuracy, hidden biases, and lack of original thought demand a more thorough re-evaluation. For businesses and content creators, understanding these patterns is crucial for maintaining authenticity and trust in an increasingly AI-driven digital landscape. #AIdetection, #WikipediaAI, #GenerativeAI, #ContentQuality

Is the "Prompt Engineer" role dead? Not dead. Rebranded. It's the core of the new AI stack. You can build the world's best engine (context), but it's useless without the key (the prompt). A great prompt is still the difference between garbage output and game-changing results. It boils down to this: The Prompt is the command. It tells the AI what to do. The Context is the power. It gives the AI the knowledge to do it well. You need both. But it all starts with the command. Master the prompt, then scale it with context. That's the winning formula. #PromptEngineering #ContextEngineering #AI #ArtificialIntelligence #FutureOfWork #TechTrends

RAG improves LLMs by pulling external knowledge, but pure vector similarity search has limits , it may retrieve text fragments without understanding entity relationships. A Knowledge Graph solves this by structuring data as nodes (entities) and edges (relationships). This lets AI reason beyond text: Resolve ambiguities e.g., Apple = fruit vs. Apple = company Ensure context aware retrieval instead of raw keyword matching In short, Knowledge Graphs make RAG more accurate, connected, and trustworthy. #RAG #KnowledgeGraph #LLM #AI #SemanticSearch #GenerativeAI

Ever wonder how AI provides answers that are both intelligent and factually accurate? This diagram shows the magic behind a powerful technique called Retrieval-Augmented Generation (RAG). The workflow you see is Retrieval-Augmented Generation (RAG). It's the key to making AI assistants more reliable. 🤔 Query -> 🔍 Find Facts -> 📝 Add Context -> 💡 Generate Answer By forcing the AI to "look it up" before speaking, RAG connects powerful language models to real-time, custom data. This means more accuracy and fewer "hallucinations." A crucial step forward for practical AI applications! #AI #Tech #RAG #LLM #ArtificialIntelligence #FutureOfWork

🚨 New Blog Series: The Evolution of AI AI didn’t begin with ChatGPT—and it won’t end there. In the first post of our new series with Lineal’s Lead Data Scientist, Matthew Heston, we take a tour through the eras of AI—from rules-based systems to statistical models, deep learning, and today’s LLMs. Why does this history matter? Because the smartest solutions don’t rely on hype. They use the right mix of methods—balancing cost, speed, and accuracy. Read Volume 1: A Short History of AI: Why LLMs Aren’t the Whole Story 🔗 https://lnkd.in/e3bt_g63 Stay tuned: the next post shows how Lineal applies these lessons in Amplify™ Review to solve real-world legal challenges. #AI #ArtificialIntelligence #MachineLearning #LegalTech #DataScience #AIEvolution #eDiscovery

What I’m Reading This Week: AI Evals 📝 So what are AI Evals? Evals are like regression tests for your AI model's output. You need to be able to get the right answer with every try. Recently, OpenAI has addressed the same issue as well! "Why Language Models Hallucinate", which talks about how the current benchmarks reward accuracy (how many questions you get right) only, not whether you're honest about what you don't know! It also talks about a new way to evaluate AI, which rewards "I don't know the answer to this question" than false answers. Examples of Eval approaches could be: Human feedback loops (thumbs up or thumbs down), code-based evals for code generation outputs. Link to the articles are in comment box. Follow for more insights on AI #AI #Evaluation #GenerativeAI !