"Generative AI and RAG for Beginners: A Practical Step-by-Step Guide to Building LLM and RAG Applications with LangChain and Python" Get your copy at http://
amzn.to/3MZZ9R5 Independently published: December 2025
Print length: 255 pages
GENERATIVE AI
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Practical Guide to Building LLM and RAG Apps with LangChain
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AI Agents and World Models: Research on Predictive Planning
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Can AI agents see into the future before acting? A team from UIUC, THU, JHU, and Columbia tested exactly that. They gave agents generative world models—external simulators that could predict outcomes before taking action. The result? Most agents refuse to simulate (under 1%
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Ways to Train an LLM
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Ways to Train an #LLM
by @goyalshaliniuk #GenAI #ArtificialIntelligence #MachineLearning #ML -
Generative AI populates 3D metaverse canvases
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Generative AI is actually going to populate the otherwise empty 3d canvas that is the so called metaverse https://t.co/JKx2IhtrEg
— Bilawal Sidhu (@bilawalsidhu) 10 mai 2026Generative AI is actually going to populate the otherwise empty 3d canvas that is the so called metaverse
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Popular AI writings from 2022-2023 still influence current models
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I suspect there was a moment, probably 2022-2023, where anything you wrote publicly about AI that was popular is likely to still have influence over current models. Since then, the open internet has become less key to training but the models remain very 2022-brained in many ways
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Mitigating AI Hallucinations Through Prompt Engineering Strategies
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7. Never trust tone as a reliability signal. Opus 4.7 is unusually confident when it's wrong. It told one user "strawberry" has two Ps. Build verification INTO your prompts: → "cite the source for every claim"
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Optimizing AI Model Verbosity Through Prompt Engineering
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5. Override verbosity. Explicitly. Opus 4.7 defaults to thorough. If you want concise, you have to ASK. "Summarize this article" "Summarize in 3 bullets. No preamble. Output directly." You get what you specify. Never what you assume.
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Optimizing LLM response quality using XML tags as attention anchors
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3. Switch from markdown to XML tags. Opus 4.7 responds to XML structure the way other models respond to markdown headers. Wrap your sections in , , , . The model treats them as attention anchors. Same content, dramatically better
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Improving AI Prompt Precision for Better Model Performance
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2. Stop writing prompts that assume the model "gets it." 4.6 prompt: "help me with my pitch deck"
(4.6 inferred you wanted structure + hooks + content tips) 4.7 prompt: "review the 10 slides below. flag weak hooks, missing stats, and confusing transitions. output as a -
Adjusting Prompting Strategies for Opus 4.7 Behavior Changes
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The shift is simple but devastating: Opus 4.6 inferred your intent and filled in the gaps.
Opus 4.7 does EXACTLY what you ask. Nothing more. Translation: the same prompt now produces narrower, terser, sometimes broken results. Here's how to fix it